In order to solve the problems of structurally complex, numerous parameters, high configuration required for training, low transmission frames of real-time detection pictures and difficult to achieve industrial application popularization of YOLOv4 target detection network, a lightweight target detection network SL-YOLO based on YOLOv4 is proposed. It improve and optimizes the original YOLOv4 network, and replaces the original backbone network of YOLOv4 with ShuffleNetv2 lightweight network, integrates SENet module into ShuffleNetv2, reduce the network computing complexity, add Swish activation function to the network layer to make the model convergence effect better; at the same time, the simplified weighted bidirectional feature pyramid structure is used to replace the feature fusion network of YOLOv4, aims to optimize the target detection accuracy; the importance of each channel was determined, thus the redundant pruning was performed, and the model was compressed. The result of a comparative experiment on open data set PASCAL VOC and MS COCO shows that the memory of the model is compressed by 89.4%, the amount of floating-point operations of the model is reduced by 88.4%, and the detection speed of the model is increased by nearly two times, which indicates the SL-YOLO lightweight network can effectively reduce the amount of model reasoning calculation and improve the model detection speed simultaneously, and greatly improve the speed of model detection.