Mobile target detection algorithms require fewer model parameters, less computation, faster reasoning speed, and better detection effects. The target detection algorithms are prone to false detection of small targets and missing detection and have insufficient ability for feature extraction. To this end, this study proposes a lightweight small target detection algorithm based on YOLOv5. In this algorithm, the lightweight network MobileNetV2 is used as the backbone network of the target detection algorithm to reduce the number of parameters and calculation amount of the model. The deep separable convolution combined with a large convolution kernel is applied to decline the number of parameters and calculation amount, and improve the detection accuracy of small targets. GhostConv is adopted to replace part of common convolution to further decrease the number of parameters and computation amount. Multiple comparison experiments are carried out on VOC competition data sets and COCO competition data sets. The results show that compared with other models, the proposed algorithm has fewer parameters, less computation, faster reasoning speed, and higher detection accuracy.