Abstract: For the problems of high hardware requirements, complex model and low accuracy of commodity image detection algorithm based on mobile platform, an improved network based on YOLOv4tiny is proposed, which can reduce the network parameters and model size, improve the network accuracy and build a more efficient network. The original standard convolution was replaced by point convolution and depth convolution, and CG module was used for feature extraction to reduce the calculation loss of network model. In feature fusion, PANity module was added to the original feature pyramid (FPN) to shorten the span of convolution layer between high and low. The CSPConcat structure was used to optimize the fusion features of each layer, which improves the ability of feature fusion. k-prototypes algorithm was used to optimize the size and number of prior boxes in daily commodity data set. Through the experiment on the daily commodity data set under the framework of Darknet deep learning, it is concluded that the average accuracy (map) of the improved algorithm is 98%, the recall rate is 97%, which is 2.4 and 2 percentage points higher than the original network, the calculation amount of the network model is 40.4% lower than the original network, and the storage file of the model is 55.9% smaller. The experimental results show that the improved network model is lighter and more accurate, which is more suitable for low hardware level embedded devices deployed in unmanned settlement link.