关键词: 死淘鸡识别, 深度学习, 轻量化网络, MobileNet, 深度可分离卷积

Abstract: Aiming at the problems that there are few studies on dead chicken target detection and the large size of the highprecision detection algorithm makes it difficult to deploy to mobile devices, a lightweight dead chicken target detection algorithm based on YOLOv4 is proposed. Firstly, the team collects images of dead chickens in cages from largescale egg production plants to build a target detection dataset. Then, MobileNetv3 backbone extraction network with depthseparable convolution is introduced in the algorithm to reduce the model size. Finally, a selfattentive mechanism module is added before the maximum pooling layer to enhance the algorithms capture of global semantic information. Experimental results in a selfbuilt dataset show that the improved algorithm has higher accuracy in the dead pheasant target detection task, with mAP values and recall rates of 97.74% and 98.15% respectively. The model size is reduced to 1/5 of the original algorithm, and the frame rate reaches 77 frames/s under GPU acceleration, doubling the detection speed and meeting the requirements of embedded deployments.

Key words: identification of dead chicken, deep learning, lightweight network, MobileNet, deep separable convolution