International Journal of High Performance Computing and Networking
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International Journal of High Performance Computing and Networking (1 paper in press)
Analysis of various versions of You Only Look Once: a comparative analysis by Ritika Dhiman, Sunil K. Singh, Gurkanwal Singh Kang, Nandini Sidana Abstract: You Only Look Once (YOLO) is the best-in-class real-time object detection algorithm that uses convolutional neural networks (CNN) to detect an object. YOLO has been very popular among the computer vision research community and has gradually improved through various iterations. It is used in a wide range of applications: to detect animals, people, objects on road, etc. YOLO accomplishes high accuracy and can provide results run in real-time where other object detection algorithms do not. The main purpose of this paper is to discuss all the various versions of the YOLO family and do a comparative performance analysis. The content of this paper includes several stages, such as summarising the development of the YOLO family, introducing their methodology, and discussing differences in their different versions. Further, YOLOv5 being the best among all other versions based on speed and accuracy has been used to experimentally detect wildfire smoke from images. Keywords: object detection; computer vision; YOLO; deep learning; image processing; convolutional neural networks; smoke detection.