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.