Smooth Surface Defect Detection by Deep Learning Based on Wrapped Phase Map

Abstract

Object detection is widely used in the industry for visual inspection of defects. But it fails to fully reflect the defects on the smooth surface due to the difficulty in illuminating, and the high performance of defect detection tends to be a challenge. In this paper, we proposed a novel defect detection method for laptop panels. On the one hand, we utilized the wrapped phase map obtained by phase-shift reflection fringes for defect detection and used the DenseFuse network to fuse the wrapped phase map with the fringe modulation map to optimize the dataset. On the other hand, we proposed an improved deep learning-based network Vovecwnet. The experiments indicated that our image-based multi-information fusion system could achieve excellent performance in defect detection on the smooth surface (e.g., laptop panels surface), with high accuracy of 97.5% and a few reference time 0.0762s on GTX 2080ti GPU. Thus, our proposed method outperformed and tackled the challenges encountered in the defect detection of smooth surfaces.