335 research outputs found

    A Comprehensive Review of YOLO: From YOLOv1 and Beyond

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    YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.Comment: 31 pages, 15 figures, 4 tables, submitted to ACM Computing Surveys This version includes YOLO-NAS and a more detailed description of YOLOv5 and YOLOv8. It also adds three new diagrams for the architectures of YOLOv5, YOLOv8, and YOLO-NA

    Reducing waste generated by international students- mobility-improving user experience with object detectionn

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    International student mobility is on the rise. Even though international exchange programs foster intercultural development, some contend that the environmental impact must be considered more. This work project focuses on one piece of the complex puzzle: overconsumption. In line with SDG 12, we propose an application called Repurpose, aimed at effectively repurposing everyday items new international students buy but would otherwise throw away at the end of their curriculum. The application is tailored to the global Gen Z audience, leveraging AI for convenient experiences and considering the time gap when international students leave and arrive. Due to its scalable SaaS nature and considerate business model, the app has the chance to grow into a critical application for international students worldwide. This report contains both common part prepared by the project team together and an individual part that was about object detection and how it can be implemented in a low code environment to improve the user experience

    Model Compression Methods for YOLOv5: A Review

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    Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and efficiency. While the evident merits of YOLO have yielded to its extensive use in many areas, deploying it on resource-limited devices poses challenges. To address this issue, various neural network compression methods have been developed, which fall under three main categories, namely network pruning, quantization, and knowledge distillation. The fruitful outcomes of utilizing model compression methods, such as lowering memory usage and inference time, make them favorable, if not necessary, for deploying large neural networks on hardware-constrained edge devices. In this review paper, our focus is on pruning and quantization due to their comparative modularity. We categorize them and analyze the practical results of applying those methods to YOLOv5. By doing so, we identify gaps in adapting pruning and quantization for compressing YOLOv5, and provide future directions in this area for further exploration. Among several versions of YOLO, we specifically choose YOLOv5 for its excellent trade-off between recency and popularity in literature. This is the first specific review paper that surveys pruning and quantization methods from an implementation point of view on YOLOv5. Our study is also extendable to newer versions of YOLO as implementing them on resource-limited devices poses the same challenges that persist even today. This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.Comment: 18 pages, 7 Figure

    An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

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    Cardiovascular diseases are one of the most important issues facing the people and their origins also death is contained all over the world the facing issues in past 25 years. Every country’s inversing large amount in health care researches and it’s related to enhanced predict the diseases. Cardio issues are not even physicians can easily be predicted and it is a very challenging task that requires high knowledge and expertise. To identify to create machine language models used to efficiently predict the earliest stage of cardiovascular disease. In this work, we recommend AWMF filter for the pre-process the Input Image after the input move to YOLOv4 neural network method for classification and segmentation to the heart affected areas by using ultrasonic Images with the help of a machine learning algorithm. The proposed algorithm uses ultrasonic picture classification and segmentation to detect cardiovascular disease earlier. This model shows the more accurate result on 96% of training and 98% testing data. And this method shows better results and providing while compared to the existing method

    Comparative Review of Object Detection Algorithms in Small Single-Board Computers

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    Object detection is a crucial task in computer vision with a wide range of applications. However, deploying object detection algorithms on small single-board computers (SBCs) poses unique challenges. In this review article, we present an in-depth comparative analysis of object detection algorithms tailored for small SBCs. We have conducted an extensive literature review on existing research in object detection algorithms and evaluated the performance of different approaches on benchmark datasets. Our review encompasses cutting-edge deep learning methods, which are YOLO, SSD, and Faster R-CNN. We delve into the challenges and limitations of implementing these algorithms on small SBCs and offer recommendations for optimizing their performance in such environments. Our analysis aims to shed light on the strengths and weaknesses of various object detection algorithms for small SBCs, ultimately guiding practitioners in making informed decisions and identifying potential avenues for future research in this domain

    Digital Twin of a Network and Operating Environment Using Augmented Reality

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    We demonstrate the digital twin of a network, network elements, and operating environment using machine learning. We achieve network card failure localization and remote collaboration over 86 km of fiber using augmented reality

    Improving the performance of object detection by preserving label distribution

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    Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.Comment: Code is available at https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai
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