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Surface defect detection of industrial products is the key link of product quality assessment in industry. To realize rapid, accurate and efficient detection is of great significance to improve industrial capacity. In this paper, a residual network model based on the combination of multi-scale feature fusion and RBN is proposed to solve the problems of traditional neural networks such as single feature scale extraction, large number of parameters and low network training efficiency. Firstly, the multi-scale convolution feature fusion module is used to extract the feature information of different scales. Then, by introducing RBN layer, the distribution of feature server is more uniform. Finally, global average pooling is used instead of the traditional full connection layer to reduce the parameters of the model, and the direct mapping between output channels and feature categories is realized. The network model proposed in this paper was tested on NEU-DET, an open data set, and the recognition rate reached 100%. The recognition rate on aluminum product defect data set of Tianchi artificial intelligence competition reached 96.67%. The model has excellent performance and can well complete the task of industrial product surface defect detection.