In order to make the model lightweight and facilitate the embedding of mobile devices, the YOLOv4 network is improved. Firstly, MobileNetV3 is used as the backbone network, and a deep separable convolution is adopted to replace the ordinary convolution of an enhanced feature extraction network, so as to reduce the number of model parameters. Secondly, when the feature map with a size of 104×104 is output, the dilated convolution with a dilated rate of 2 is fused, and it is then fused with a feature layer with a size of 52×52, so as to obtain more semantic and location information, which can refine the feature extraction ability and improve the detection performance of the model for minimal targets. Finally, the original pooling layer is connected in series with three Maxpools with a size of 5×5 to reduce the computational load and improve the detection speed. The experimental results show that on Huawei Cloud 2020 dataset, the mAP of the improved algorithm is improved by 2.33% compared with the YM algorithm, and on the public dataset VOC07 + 12, the mAP is improved by 3.12%, and the FPS has more than doubled compared with the original YOLOv4 algorithm, with the number of parameters reduced to 18% of the original one. As a result, the effectiveness of the improved algorithm is verified.