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YOLO-based Object Detection Models: A Review and its Applications

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Abstract

In computer vision, object detection is the classical and most challenging problem to get accurate results in detecting objects. With the significant advancement of deep learning techniques over the past decades, most researchers work on enhancing object detection, segmentation and classification. Object detection performance is measured in both detection accuracy and inference time. The detection accuracy in two stage detectors is better than single stage detectors. In 2015, the real-time object detection system YOLO was published, and it rapidly grew its iterations, with the newest release, YOLOv8 in January 2023. The YOLO achieves a high detection accuracy and inference time with single stage detector. Many applications easily adopt YOLO versions due to their high inference speed. This paper presents a complete survey of YOLO versions up to YOLOv8. This article begins with explained about the performance metrics used in object detection, post-processing methods, dataset availability and object detection techniques that are used mostly; then discusses the architectural design of each YOLO version. Finally, the diverse range of YOLO versions was discussed by highlighting their contributions to various applications.

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Acknowledgements

The authors are grateful to the Ministry of Heavy Industries (MHI), Government of India, for their funding support under the Scheme for Enhancement of Competitiveness in the Indian Capital Goods Sector Phase II. The authors express their gratitude to SASTRA Deemed University, Thanjavur, for providing the infrastructural facilities to carry out this research work.

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Vijayakumar, A., Vairavasundaram, S. YOLO-based Object Detection Models: A Review and its Applications. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18872-y

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