Lightweight object detection algorithm for automatic driving scenarios Online publication date: Wed, 07-Feb-2024
by Zhou Tu; Ming Chen
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 26, No. 1, 2024
Abstract: Aiming to solve the problems of large model size, unbalanced detection speed and detection accuracy of traditional target-detection algorithms in autonomous driving scenarios, a lightweight YOLO algorithm is proposed based on YOLOv4. First, the lightweight network Mobilenetv1 was used to replace the original YOLOv4 feature-extraction network and a Depth-Wise Cross-Stage Part module (DW-CSP) was proposed to improve the detection speed. Then, a new Lish activation function was designed and the K-means++ clustering algorithm was used to regenerate prior frames of different scales. Finally, the FocalLoss loss function was introduced instead of the cross-entropy loss function. Experiments show that compared with the YOLOv4 algorithm, the improved YOLO algorithm improves the detection accuracy by 1.72% and the detection speed by 53%, and the model size is reduced by about four times. The algorithm is more in line with the target-detection requirements of autonomous driving scenarios.
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