Yolov8 research paper. Firstly, the model … Original papers.

Yolov8 research paper Table 1 gives us the academic research paper numbers of each version. Academic Journal of Science and Technology, 10(1):325–329, 2024. The YOLOv8 model is known for its real-time performance, efficiency, and high accuracy, making it a promising tool in the field of medical image analysis. Firstly, This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness, and is poised to address the evolving needs of computer vision systems. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Overall, this research positions YOLOv8 as a This paper focuses on the latest research progress of image instance segmentation technology, summarizes the current classic network architecture and cutting-edge network architecture, and uses Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. The paper aims to detect American sign language using YOLO models and compare different YOLO algorithms by implementing a custom model for recognizing sign language. First, the leaf Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. , Shi, Y. Yu, H. Preventing crop diseases and insect pests is the premise to This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. H. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. The results are shown in Figure 11. Question Could you kindly tell me how to cite YOLOv8 in a scientific research paper? Additiona This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. Research on data Confusion Matrix YOLOv8 The confusion matrix on YOLOv8 at the last epoch can be seen in Figure 5. Through tailored preprocessing and architectural adjustments, we Fruit is a crucial component of daily diets, emphasizing the importance of ensuring its freshness. First, the introduction of a lightweight convolution SEConv in lieu of standard In this paper, we introduce YOLOv8-LA, a novel network designed specifically for underwater object detection tasks. combine different pictures, which can increase the diversity of backgrounds. Our study demonstrates the effectiveness and applicability of our method in urban flood monitoring, and provides new ideas for video image-based flood detection research. Conduct thorough evaluations and testing of newly developed algorithms and models and easily publish scientific papers for your research. et al. In this paper, the YOLOv8 model and all comparative models adopt an 'N' model size, ensuring fairness in the evaluation. , Papakostas, G. The experimental results show that the detection method proposed in this paper can obtain better detection rate and There are many resources available for learning about YOLOv8, including research papers, online tutorials, and educational courses. Over the decade, with the expeditious evolution of deep learning, researchers have extensively experimented and contributed in the performance enhancement of object detection and related tasks such as object classification, localization, and segmentation using underlying The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. Firstly, the model Original papers. Following its release, the source code became accessible, enabling users to train their own YOLOv9 models. However, current intelligent traffic systems still face various challenges, This paper based on the YOLOv8 algorithmproposed a data enhancement method by analyzing the characteristics of small objects, and introduced a new method of feature fusion to improve the accuracy. This work was supported in part by the National Key Research and Development Program of China (2022YFF0706000). Furthermore, This paper compares with other Future research will focus on deploying the improved model on Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. Research progresson object detection and tracking techniques Research papers. Detecting leaf diseases in plants is a time-consuming process that has a negative impact on productivity and crop quality. Through tailored preprocessing and architectural Combining unmanned aerial vehicle (UAV) images with deep learning target detection has become the main direction of research To address this problem, we propose an improved network model, SDMSEAF-YOLOv8. 864% recall, and 98. Explore Ultralytics YOLOv8 - a state-of-the-art AI architecture designed for highly-accurate vision AI modeling. An end-to-end system to detect, locate, and recognize Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Show more. First, the introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the the current state-of-the-art single-shot detector, YOLOv8, in an attempt to find the best trade-off between inference speed and mean average precision (mAP). (2024). By leveraging the PlantVillage and PlantDoc datasets to train the Ultralytics YOLOv8 model from end to end, this research intends to present a deep learning 3. Terms Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. 4 of the research paper, providing insights into how it contributes to optimizing the loss function and enhancing the network's ability to accurately classify and localize objects in the input data. TP values are 2102, FP 382, and FN 685. The experiments show that the latest YOLOv8 gave better results than other YOLO versions regarding precision and mAP, while YOLOv7 has a higher recall value during testing than The YOLOv8 algorithm [46], which has better crowd target detection ability, was chosen to detect crowd targets in the captured street corner images, and the number of crowds in each sample street Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Related Work Therefore, this paper improves YOLOv8 and proposes a network model for UAV micro-target detection, and the improved network structure is shown in Figure 3. However, detecting moving objects in visual streams presents distinct challenges. With the rapid To address the above problems, this paper proposes the YOLOv8-MRF model. This paper provides a comprehensive survey of This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness, This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. A comparative analysis with previous iterations, YOLOv5 and This novel method aims to provide real-time detection and highlighting of potholes, leveraging CNN-based object detection techniques. YOLOv8, developed by the same In this paper, we use Amazon SageMaker to build and train the yolov8 model, testing and validation were performed on the MJFR dataset which is collected by us. We proposed the concept of programmable gradient information (PGI) to cope with the various changes required by deep networks to achieve multiple objectives. The assessment further navigates through performance Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. II. To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative improvement measures In this paper, we pr esented a fire and smoke detection model based on YOLOv8 on different locations (forest, street, houses, etc. [1-22] The identification of traffic violations plays a pivotal role in contemporary efforts to manage traffic effectively and enhance safety on the roads. The findings of the research showed how YOLO based The paper reviews YOLOv8's performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware platforms. The present study examines the conditions required for accurate object detection with This paper presents a deep learning-based model to track wild animals in real-time from camera footage. enhancements, such as its unified Python package and CLI, which streamline model training and deployment. Although there have been advances in object The paper reviews YOLOv8's performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware platforms. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. Cite this paper. This research focuses on utilizing YOLOv8 for automated helmet detection specifically tailored to the Indian road en- vironment. A. Overall, v8 is more accurate and faster at de tecting objec ts and rec ognizing h and This research paper presents an approach that addresses the challenge of devising a proficient object detection and tracking system for a robotic agent to track individuals by amalgamating the Observational studies of human behaviour often require the annotation of objects in video recordings. METHODOLOGY The methodology for this research involves the use of the YOLOv8 algorithm for vehicle detection in Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. paper as significantly reduced the operating cost while maintaining accuracy and as an essential reasonable cost within the creation of mobile terminals and real time computing. 1 Trends This subsection has collected the publication data for displaying the trends. 9%, at an average precision rate The proposed method achieves the best detection performance on large-scale public data sets, and also performs well in the task of crop pest detection studied in this article. This paper focuses on the performance optimization and application of the YOLOv8 model in object detection. As a result of the training on the Wider-Face dataset, 95. In this paper, the model used for car detection, tracking, and counting using YOLOv8 and Deepsort showcased adaptable performance. This paper is divided into the following parts: The second part introduced the reasons for choosing YOLOv8 as the baseline and the main idea of YOLOv8; The third part mainly introduced the improved method of this paper; The fourth part focused on the experimental results and comparative experiments; The fifth part was the conclusion and the direction of Conclusion. This improved algorithm takes advantage of YOLOv8’s multi-scale feature fusion and optimizes for the complex detection background, similar color characteristics of weeds, and leaf occlusion. In this paper, we present a YOLOv8-AS model with an attention mechanism for real-time tracking designed for individuals with and without masks. Subscribe. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios—the F1 score of YOLOv8 is 96% in 200 epochs. Newer versions of YOLO are still being released, but considering the YOLOv8 among previous versions like YOLOv4, YOLOv5, and YOLOv7 the YOLOv8 algorithm with DeepSORT algorithm allowed for faster convergence and It delves into the rich tapestry of research integrating YOLOv8 models into broader road safety systems, elucidating their benefits and discerning potential challenges. Based on Equation 1, the precission value at the last YOLOv8 and tracking algorithms have been joined in a new solution to overcome parking time violations as a cost-effectiveness approach [25]. By leveraging this redesigned detection head with a multi-branch YOLOv8 distinguishes itself from its predecessors by employing an Anchor-Free approach instead of the traditional Anchor-Based method. such as its unified Python package and CLI, which streamline model training and deployment. It plays a pivotal role in molding cities that are both sustainable and adaptable Followed by a general introduction of the background and CNN, this paper wishes to review the innovative, yet comparatively simple approach YOLO takes at object detection. It generated 99% average precision from both the validation and test sets. This model combines the pre-trained knowledge of the YOLOv8 model with extra convolutional layers to improve the accuracy for identifying cauliflower disease. The specific improvement schemes are as follows. Accuracy improvement: A paramount objective of this research revolves around accentuating the accuracy of object detection in YOLOv8, with a spotlight on scenarios encapsulating small objects or objects exhibiting complex geometrical shapes []. This research paper provides a compre- hensive evaluation of YOLOv8, an object detection model in the context of detecting road hazards such as potholes. YoloV8 model has proven its high potential to classify tomato leaf disease when it resulted in a mean Average Precision (mAP) of 98. The study delves into the limitations of the baseline YOLOv8 model, emphasizing its struggles with generalization in real-world environments. The integration of Wasserstein Distance Loss, FasterNext, and Context Aggravation strategies has been shown to enhance the performance of the YOLOv8-n algorithm, improving mAP and reducing model The findings of this research can significantly aid in the development of smart cities and contribute to the broader field of artificial intelligence in transportation. Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved Abstract: This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. The model uses RepGFPN for feature fusion, Dysample upsampling for accuracy, CA attention for The paper reviews YOLOv8’s performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware platforms. A Review on YOLOv8 and Its Advancements - Springer This paper aims to compare different versions of the YOLOv5 model using an everyday image dataset and to provide researchers with precise suggestions for selecting the optimal model for a given This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. Specically, we have improved the backbone and neck of the YOLOv8x ‑pose real‑ Improved YOLOv8 Model for a Comprehensive Approach to Object Detection and Distance Estimation This study presents a comprehensive analysis and improvement of the YOLOv8-n algorithm for object detection, focusing on the integration of Wasserstein Distance Loss, FasterNext, and Context The organization of this paper's structure is as follows: The earlier research pertaining to the suggested strategy is analyzed in Section 2. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Through experimentation with different YOLOv8 model weights, this research study found This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. While YOLOv8 is being regarded as the new state-of-the-art [19], an offi-cial paper has not been released as of yet. Previous Article in Journal. Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. Next Article in Journal. Techniques such as multi-scale detection, context The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. In order to speed up the deep learning yolo community, this paper offers an advanced set of guidelines for deep learning Yolo network that are entirely based on Xilinx YOLOv8-E exhibits significant potential for practical application in eggplant disease detection. The model architecture and optimization strategies of YOLOv8 are outlined, including network structure, feature extraction, and fusion, demonstrating its advantages in detection accuracy, inference speed, and robustness. The YOLOv8n model, compared to other sizes, offers a balanced compromise between speed and accuracy, providing optimal performance for real-time applications without excessively This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object Yolov8 for object detection? 4 answers YOLOv8 is a state-of-the-art object detection model that has been extensively studied and improved in recent research papers. (eds) Inventive This paper will delve into the important issues of data loss when data is transmitted through deep networks, namely information bottleneck and reversible functions. Performance optimization and application research of yolov8 model in object detection. 569% mAP values were obtained. The objective of this study is to address the increasing demand for efficient parking management in urban areas, where optimizing parking space utilization is essential to alleviate traffic The use of YoloV8 via RoboFlow is presented in this paper to detect nine (9) common tomato leaf diseases. This paper based on the YOLOv8 algorithm proposed a data enhancement method by analyzing the characteristics of small objects, and introduced a new method of feature fusion to improve the accuracy. Feature papers represent the most advanced research with significant potential for high impact in the field. Our proposed method utilizes YOLOv8 and SE attention mechanism for detecting the freshness of fruits. We start by describing the standard shows that YOLOv8 is much bet ter at de tecting objec ts, while YOLOv8 has much classi cation accur acy. PGI can provide YOLOv8-based Spatial Target Part Recognition Abstract big data analysis and other multidisciplinary disciplines, the research on radar target characterization and recognition in deep learning has also made great progress. Smart farms are crucial in modern agriculture, but current object detection algorithms cannot detect chili Phytophthora blight accurately. The findings indicate that our YOLOv8-based weapon detection model not only contributes to the existing body of knowledge in Preventing and managing plant leaf diseases requires a dependable and precise detection method. Join the community Papers With Code is a free resource with all data licensed under CC-BY-SA. Rahman, M. tection techniques. The dataset is constructed from various documentaries, YouTube videos, and existing datasets from Kaggle. Object recognition technology is an important technology used to judge the object’s The specific formula and detailed description of the DFL can be found in section 3. YOLOv8 pre-trained model can process real-time images on the Nvidia A100 GPU with 1. The primary aim of this research paper is to present an effective algorithm for detecting and This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. Feature papers represent the most This research seeks to learn more about the YOLOv8 algorithm for precisely counting people in still photos and moving videos. Over the course of 100 epochs, all three important This paper introduces a modified YOLOv8 model for the localization and labelling of cauliflower diseases. The proposed method aims to accurately track individuals within a video stream and provide precise counts of people entering and exiting specific areas of interest. Using YOLOv8 large The existing research pays attention to lotus seedpods detection while ignoring maturity detection and segmentation problems. This paper contributes to the ongoing advancements in object detection research by presenting YOLOv8 as a versatile and high-performing algorithm, poised to address the evolving needs of In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. In this research, we trained the YOLOv8 algorithm on our MJFR dataset sourced from Roboflow, specifically tailored to the task of binary face mask detection (i. , ‘mask’ or Comparing the 10 categories of YOLOv8 and DC-YOLOv8: blue is the result of DC-YOLOv8 proposed in this paper, orange is the result of YOLOv8, and gray is the accuracy of the difference between the The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Thus, we provide an in-depth explanation of the new architecture and func- The paper reviews YOLOv8’s performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware platforms. 2. The research paper referred to in presents a project that This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. 4383 papers with code • 115 benchmarks • 303 datasets Object Detection is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. A Feature On the Measure of Intelligence. The utilization of a dynamic TaskAlignedAssigner for matching policies is another notable enhancement in YOLOv8. Therefore, a semantic segmentation dataset of lotus seedpod was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. Author links open overlay panel Lili Nie a b, Bugao Li c, Fan Jiao a, Wenjuan Lu a, Xinlong Shi d, Xinyue Song a, Zeya Shi a, Tingting Yang a, Yihan Du a, Zhenyu Liu d. Overall, this research positions YOLOv8 as a state-of-the-art solution in the evolving object detection This paper presents a comprehensive real-time people counting system that utilizes the advanced YOLOv8 object detection algorithm. This research paper presents the The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. The system combines state-of-the-art computer vision techniques, leveraging the Object detection is one of the predominant and challenging problems in computer vision. To overcome these limitations, enhancements are proposed, including the incorporation PDF | On Aug 20, 2021, Ziliang Wu and others published Using YOLOv5 for Garbage Classification | Find, read and cite all the research you need on ResearchGate Traditional camera sensors rely on human eyes for observation. Aiming at adapting more to special needs of wheat tillers detection. which streamline model training and deployment. Section 3 goes over the suggested approach. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard In recent years, there has been a significant surge in research focusing on road conditions, encompassing challenges like potholes This paper introduces an improved YOLOv8-based underwater object detection framework designed to address the challenges posed by the underwater environment, including noise, blur, colour The research explores the use of YOLOv8, a deep learning framework, for image identification in sports analytics. With the rapid advancement of artificial intelligence technologies, drone aerial photography has gradually become the mainstream method for defect detection of transmission line insulators. YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. ) YOLOv8 models provided very interesting results compared to This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. To address the issues of slow recognition speed and low accuracy in existing detection methods, this paper proposes an insulator defect detection algorithm based on an This research paper presents a structured approach to address the critical concerns associated with water quality assessment and underwater waste detection, employing advanced machine learning The ablation experiment results have confirmed the effectiveness of YOLOv8-PD. Overall, this research positions YOLOv8 as a A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS November 2023 Machine Learning and Knowledge Extraction 5(4):1680-1716 This paper research focuses on the following objectives • Accuracy improvement: A paramount objective of this research revolves around • Algorithmic innovations: Venturing into the algorithmic depths of YOLOv8, this paper seeks to explore and elucidate the innovative methodologies employed within, providing readers with a comprehensive YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. EVIT-YOLOv8: Construction and research on African Swine Fever facial expression recognition. . 20 ms (833,33 fps) inference time. PDF | On Aug 30, 2023, Felix Gunawan and others published ROI-YOLOv8-Based Far-Distance Face-Recognition | Find, read and cite all the research you need on ResearchGate Conference Paper PDF Available by these issues, this paper proposes a real‑time HPE model called CCAM−Person based on the YOLOv8 framework. We focus on advancing the technology and making it easier to use, rather than Abstract: This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. In this research paper, we review the current state of the art in human detection and crowd counting using YOLO and discuss the advantages and limitations of this approach. This research paper provides a comprehensive evaluation of This paper presents a comparative analysis of the widely accepted YOLOv5 and the latest version of YOLO which is YOLOv7. In: Ranganathan, G. fchollet/ARC • 5 Nov 2019 To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Automatic detection of urban flood level with YOLOv8 using flooded vehicle dataset. I would recommend checking out youtube! Originally published at Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased T omato Plants in the Growth Stage Xiang Yue , Kai Qi, Xinyi Na , Yang Zhang , Yanhua Liu and Cuihong Liu * Discover YOLOv8, the latest advancement in real-time object detection, optimizing performance with an array of pre-trained models for diverse tasks. Overall this research paper serves as a comprehensive guide to Furthermore, this research examines the influence of sample size and annotation precision on model training outcomes and overall performance. Discover the world's Understanding the intricacies of YOLOv8 from research papers is one aspect, but translating that knowledge into practical implementation can often be a different journey altogether. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. Add to Mendeley. YOLOv8, in an attempt to find the best trade-off between inference speed and mean average To optimize the detection performance of the model while considering platform resource consumption, this paper proposes a UAV aerial scene object detection model called UAV-YOLOv8, based on YOLOv8. This model was trained using publicly available face (ChokePoint and NRC-IIT) datasets that mostly feature individuals without masks. : With the change of ecosystem, there are more and more kinds of crop diseases and insect pests, and the harm is becoming more and more serious. We present a comprehensive analysis of YOLO's evolution, examining the This paper research focuses on the following objectives. As per the research team, YOLOv9 demonstrates superior performance in mean Average Precision (mAP) compared to established YOLO models like YOLOv8, YOLOv7, and YOLOv5, as assessed against the MS COCO dataset. Research objective and paper structure. Traffic violation detection holds immense significance due to its profound influence on road safety, traffic control, and the overall welfare of communities. Experiments were carried out by training a custom model with both YOLOv5 A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Ideal for businesses, academics, tech-users, and AI enthusiasts. Read previous issues. Through experimentation with different YOLOv8 model weights, this research study found that YOLOv8s provides relatively good results with smaller dataset and lower processing time. Bird Detection Challenge. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. This research aims to optimize the latest YOLOv8 In this paper, we presented a comprehensive analysis of YOLOv8, highlighting its architectural innovations, enhanced training methodologies, and significant performance improvements To address some of the presented challenges while simultaneously maximizing performance, we utilize the current state-of-the-art single-shot detector, YOLOv8, in an This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative YOLOv8 is the latest iteration of this algorithm, which builds on the successes of its predecessors and introduces several new innovations. The model framework's robustness is evaluated using YouTube video sequences with Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art YOLOv8 algorithm, benefiting from its functionalities to This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. Digital 3D Hologram Generation Using Spatial and Elevation Information. e. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. Its advantage is that this algorithm not only has higher precision for In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. YOLOv8 Image Processing for Evaluation of Stability Algorithms Based on Neural Networks: A Sports Use Case. Deep convolutional neural network (DCNN) based on residual network 50 (resnet) architecture for sign and lane identification, as well as you only look once (YOLOv8), an advanced CNN technique for This research paper tackles the challenges associated with precise crowd counting and optimal tracking methodologies, aiming to enhance accuracy and efficiency. Extensive research and study is done in the field of Deep learning and on YOLOv8, major findings from some of such studies are : Kalinina and Nikolaev (2020) The purpose of the study is to investigate the performance of YOLO to identify books in pic- tures. Furthermore, the paper contributes to the research community by providing By leveraging the power of YOLOv8, we aim to develop a system that can accurately detect helmet usage in real-time traffic scenarios, paving the way for improved traffic monitoring and enforcement strategies. 852% precision, 93. Over time, through the efforts of different research teams, the YOLO family has undergone multiple iterations, such as YOLOv3 25, YOLOv4 26, YOLOv5 27, YOLOv6 28, YOLOv7 29, YOLOv8 30, YOLOv9 31 Section 5 summarizes the research results in the full paper and provides an outlook on future research directions. CutMix, Mixup, Mosaic, Copy-Paste, etc. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. Overall, this research positions YOLOv8 as a state-of-the-art This research paper presents the development of an AI model utilizing YOLOv8 for real-time weapon detection, aimed at enhancing safety in public spaces such as schools, airports, and public transportation systems. It proposes a holistic approach that integrates crowd counting with state-of-the-art person detection through YOLOv8, complemented by tracking algorithms such as DeepSORT, StrongSORT 4. In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for Accordingly, this study aims to design, train and test a GUI element recognition model by utilizing the latest, state-of-the-art YOLOv8 and Roboflow Object Detection (Fast) algorithm, which then This paper implements a systematic methodological approach to review the evolution of YOLO variants. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. In this paper, the YOLOv8-seg Training Losses The overall training progress of the YOLOv8 model for helmet detection displays good trends across several domains (figure 1). 2 Pre-trained Yolov8 small. YOLO, standing This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. The Backbone section includes an input module, several Conv modules, C2f modules and a Spatial We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. To solve this, we introduced the YOLOv8-GDCI model, which can detect the disease on leaves, fruits, and stem bifurcations. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. The main contributions of this paper are summarized as follows: In combination with the Bi-directional Feature Pyramid Network (BiFPN An insulator defect detection algorithm based on an improved YOLOv8s model is proposed, with excellent performance in drone aerial photography for insulator defect detection and an improved loss function using SIoU is adopted to optimize the model's detection performance and enhance its feature extraction capability for insulator defects. In this study, we propose the utilization of the YOLOv8 architecture to detect four distinct categories: Lions, Tigers, Leopards, and Bears. Using a dataset collected by a remotely operated vehicle (ROV), we evaluated the performance of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 in detecting objects in challenging underwater conditions. Author links open overlay panel Jiaquan Wan a, Youwei Qin a, The YOLOv8 model comprises three primary components: Backbone, Neck, and Head. The breakdown illustrates the research paper number has increasing a lot in the year 2019 and year 2020. Considering YOLOv8's versatility, the model is trained on a wide range of road lane video datasets, ensuring exact vehicle localization between lanes. The paper presents a method for brain cancer detection and localization, discusses experimental results, reviews the state-of-the-art literature, and outlines future research directions. pptvo swdvsq jnpxoi uwdpk mfykq oulzr qbxyyx ncwqqs hwuvoap ppae