Abstract
Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and assess these designs. In this paper, we show that the Yolov4-tiny architecture outperforms the Yolov4, and Yolov5s in terms of mAP@50 and computing time per image.
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