The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the defects exhibit low contrast, different textures and geometric structures, and several defects making the surface defect detection more difficult. In this paper, a pixel-wise detection framework based on convolutional neural network (CNN) for strip steel surface defect detection is proposed. First we extract the salient features by a pre-trained backbone network. Secondly, contextual weighting module, with different convolutional kernels, is used to extract multi-scale context features to achieve overall defect perception. Finally, the cross integrate is employed to make the full use of these context information and decoded the information to realize feature information complementation. The experimental results of this study demonstrate that the proposed method outperforms against the previous state-of-the-art methods on strip steel surface defect dataset (MAE: 0.0396; Fβ: 0.8485).
Keywords: Surface defect detection; convolutional neural network; cross integrate; multi-scale context information; pixel-wise detection.