Dead chicken target detection algorithm based on lightweight YOLOv4
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.
identification of dead chickendeep learninglightweight networkMobileNetdeep separable convolution
万方数据
维普
