Lightweight YOLOV4 algorithm for underwater whale detection

At present, it is difficult to implement on-line detection on underwater equipment due to the large model of biometric algorithm. In this paper, a YOLOv4 lightweight whale detection algorithm suitable for embedded equipment is proposed. MobileNetv3 was used as the backbone network of YOLOv4 to reduce the network scale, and the neck and head network were optimized by Depthwise Separable Convolutional to achieve lightweight feature extraction. Experimental results on whale data set show that compared with YOLOv4 algorithm, the number of network parameters is reduced by 87.2%, and the detection speed is improved by 1.65 times under GPU-only and 12.56 times under CPU-only. The method presented in this paper can theoretically implement underwater whale on-line detection in embedded devices.