Abstract:At present, there are few studies on dust image recognition using the deep learning method, and the recognition rate of dust images is low due to the application of some traditional methods. In view of this situation, a dust identification method based on an improved residual network is proposed. The method applies ResNet-50 network to a dust data set, and the network structure is improved. Then, spatial pyramid pooling is added to solve the problem that the size of the input images is not fixed. In addition, the pyramid pooling is changed to average pooling, and the method of expanding a feature graph is applied to the backbone network, which is conducive to extract more fine-grained features, improve the performance of the model, and increase the recognition rate. In conclusion, the proposed method has high accuracy and provides an effective scheme for dust identification.