Steel Surface Defect Detection via Deformable Convolution and Background Suppression

Abstract

Surface defect detection is of great significance to ensure the quality of steel plate. The surface defects of steel plate are characterized by multiple types, complex and irregular shapes, large scale range, and high similarity with normal regions, resulting in low accuracy of widely used vision based defect detection methods. To overcome these issues, this paper proposes a method of detecting steel plate surface defects based on deformation convolution and background suppression. First, an improved Faster RCNN method with deformable convolution and Region-of-Interest align is proposed to enhance the detection performance for large-scale defects with complex and irregular shapes; Second, a background suppression method is proposed to enhance the discrimination ability between the normal region and the defect region. Experimental results show that, compared with the state-of-the-art methods, the proposed method can significantly improve the defect detection performance of steel plate.