Abstract: For the detection of minor damages on metal surface, the generalization ability of traditional target recognition algorithms is weak, the general detection algorithms using deep convolution neural network is easy to lose the characteristics of small targets, and the traditional square structure convolution used by these algorithms is not suitable for dealing with irregular damages such as long strips. To solve the above problems, a cascade neural network target detection model based on attention mechanism and deformable convolution, called ADC-Mask R-CNN, is proposed. The model embeds channel domain attention and spatial domain attention in ResNet101 backbone network to enhance the detection effect of minor damage targets, and uses deformable convolution and deformable region of interest pooling technology to improve the detection effect of irregular damages. In addition, the detection results are further optimized by cascaded networks. Comparative experiments on metal surface damage data sets show that the ADC-Mask R-CNN model can improve the detection performance of minor irre- gular damages on metal surface.

Key words: minor damage, irregular damage, attention mechanism, deformable convolution, cascade neural network