Yolov8 research paper YoloV8 model has proven its high potential to classify tomato leaf disease when it resulted in a mean Average Precision (mAP) of 98. e. 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 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 This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. CutMix, Mixup, Mosaic, Copy-Paste, etc. combine different pictures, which can increase the diversity of backgrounds. 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 Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. The YOLOv8n model, compared to other sizes, offers a balanced compromise between speed and accuracy, providing optimal performance for real-time applications without excessively 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. Overall, this research positions YOLOv8 as a state-of-the-art solution in the evolving object detection Object detection is one of the predominant and challenging problems in computer vision. Over the course of 100 epochs, all three important This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. While YOLOv8 is being regarded as the new state-of-the-art, an official paper has not been released as of yet. This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. 5 values of 0. 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 this paper, the YOLOv8 model and all comparative models adopt an 'N' model size, ensuring fairness in the evaluation. The primary aim of this research paper is to present an effective algorithm for detecting and 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. Research progresson object detection and tracking techniques YOLOv8 distinguishes itself from its predecessors by employing an Anchor-Free approach instead of the traditional Anchor-Based method. 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. This paper focuses on the performance optimization and application of the YOLOv8 model in object detection. 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. 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. It generated 99% average precision from both the validation and test sets. enhancements, such as its unified Python package and CLI, which streamline model training and deployment. 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. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. YOLOv8 is the latest iteration of this algorithm, which builds on the successes of its predecessors and introduces several new innovations. 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. The primary aim of this research paper is to present an effective algorithm for detecting and 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. YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Although there have been advances in object 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. 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]. We present a comprehensive analysis of YOLO's evolution, examining the 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 This paper research focuses on the following objectives. 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. In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant 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. While YOLOv8 is being regarded as the new state-of-the-art [19], an offi-cial paper has not been released as of yet. First, the introduction of a lightweight convolution SEConv in lieu of standard 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 This paper introduces a modified YOLOv8 model for the localization and labelling of cauliflower diseases. 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 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). YOLOv8, in an attempt to find the best trade-off between inference speed and mean average precision (mAP). Show more. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 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 computer vision systems. Research on data This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. Bird Detection Challenge. The model framework's robustness is evaluated using YouTube video sequences with This novel method aims to provide real-time detection and highlighting of potholes, leveraging CNN-based object detection techniques. Author links open overlay panel Jiaquan Wan a, Youwei Qin a, The YOLOv8 model comprises three primary components: Backbone, Neck, and Head. 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. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. This research focuses 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 YOLOv8 distinguishes itself from its predecessors by employing an Anchor-Free approach instead of the traditional Anchor-Based method. In this study, we propose the utilization of the YOLOv8 architecture to detect four distinct categories: Lions, Tigers, Leopards, and Bears. The Backbone section includes an input module, several Conv modules, C2f modules and a Spatial 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. 9%, at an average precision rate Traditional camera sensors rely on human eyes for observation. Question Could you kindly tell me how to cite YOLOv8 in a scientific research paper? Additiona 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 detection. 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. We achieve this by training our first (generalized) model on a data set containing 40 different classes of flying objects, forcing the Research objective and paper structure. Conduct thorough evaluations and testing of newly developed algorithms and models and easily publish scientific papers for your research. To mitigate the above problems, this paper uses YOLOv8 as the base model and optimizes the model from the The identification of traffic violations plays a pivotal role in contemporary efforts to manage traffic effectively and enhance safety on the roads. which streamline model training and deployment. This paper presents a deep learning-based model to track wild animals in real-time from camera footage. The . This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. ) YOLOv8 models provided very interesting results compared to Research papers. 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 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. Firstly, the model In this paper, we introduce YOLOv8-LA, a novel network designed specifically for underwater object detection tasks. This research study provides an analysis of YOLO v8 by highlighting its innovative features, improvements, applicability in different environments, and a detailed comparison of its performance metrics to other versions and models. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the This paper implements a systematic methodological approach to review the evolution of YOLO variants. This research paper provides a comprehensive evaluation of Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. The present study examines the conditions required for accurate object detection with YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. 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 YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. The dataset is constructed from various documentaries, YouTube videos, and existing datasets from Kaggle. Yu, H. Automatic detection of urban flood level with YOLOv8 using flooded vehicle dataset. This research aims to optimize the latest YOLOv8 This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. The utilization of a dynamic TaskAlignedAssigner for matching policies is another notable enhancement in YOLOv8. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. TP values are 2102, FP 382, and FN 685. 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. 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 tackles the challenges associated with precise crowd counting and optimal tracking methodologies, aiming to enhance accuracy and efficiency. Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. 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. 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 Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. First, the introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the 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. . EVIT-YOLOv8: Construction and research on African Swine Fever facial expression recognition. 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. An end-to-end system to detect, locate, and recognize Abstract: This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. Firstly, This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. 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. Most of the current research methods generally have low accuracy for object detection in UAV aerial photography scenarios, and it is difficult to balance the relationship between the accuracy of the model and resource consumption. This research paper provides a compre- hensive evaluation of YOLOv8, an object detection model in the context of detecting road hazards such as potholes. Add to Mendeley. 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 shows that YOLOv8 is much bet ter at de tecting objec ts, while YOLOv8 has much classi cation accur acy. Experiments were carried out by training a custom model with both YOLOv5 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. Object recognition technology is an important technology used to judge the object’s 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. This research paper presents the Through experimentation with different YOLOv8 model weights, this research study found that YOLOv8s provides relatively good results with smaller dataset and lower processing time. This model combines the pre-trained knowledge of the YOLOv8 model with extra convolutional layers to Training Losses The overall training progress of the YOLOv8 model for helmet detection displays good trends across several domains (figure 1). A comparative analysis with previous iterations, YOLOv5 and 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 YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. 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 Confusion Matrix YOLOv8 The confusion matrix on YOLOv8 at the last epoch can be seen in Figure 5. Traffic violation detection holds immense significance due to its profound influence on road safety, traffic control, and the overall welfare of communities. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the This paper presents a comprehensive real-time people counting system that utilizes the advanced YOLOv8 object detection algorithm. 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. outcome of this research work was pretty good with the mAP@0. This paper provides a comprehensive survey of recent developments in YOLOv8 and discusses its potential future directions. 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. 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 []. In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8. Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. 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 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 The use of YoloV8 via RoboFlow is presented in this paper to detect nine (9) common tomato leaf diseases. Thus, we provide an in-depth explanation of the new architecture and func- Object detection is a crucial task in computer vision that has its application in various fields like robotics, medical imaging, surveillance systems, and autonomous vehicles. The experimental results show that the detection method proposed in this paper can obtain better detection rate and 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 With the rapid advancement of artificial intelligence technologies, drone aerial photography has gradually become the mainstream method for defect detection of transmission line insulators. 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. [1-22] 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. Overall, v8 is more accurate and faster at de tecting objec ts and rec ognizing h and 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. Ideal for businesses, academics, tech-users, and AI enthusiasts. Original papers. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. We start by describing the standard Explore Ultralytics YOLOv8 - a state-of-the-art AI architecture designed for highly-accurate vision AI modeling. However, detecting moving objects in visual streams presents distinct challenges. tection techniques. 984 on the dataset of 800 images split . 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. Observational studies of human behaviour often require the annotation of objects in video recordings. It plays a pivotal role in molding cities that are both sustainable and adaptable In this paper, we pr esented a fire and smoke detection model based on YOLOv8 on different locations (forest, street, houses, etc. 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 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. , ‘mask’ or There is yet to be a research paper released f or YOLO v5. The newest version of the YOLO model, YOLOv8 is an advanced real-time object detection framework, which has attracted the attention of the research community. The system combines state-of-the-art computer vision techniques, leveraging the This paper presents a comparative analysis of the widely accepted YOLOv5 and the latest version of YOLO which is YOLOv7. 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. Through tailored preprocessing and architectural 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 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 Furthermore, this research examines the influence of sample size and annotation precision on model training outcomes and overall performance. qmsn uliowd ygkf zlwi qmxbix cve mlrdg ppbgwds nnfy lfxxfv