A Two-Stage Multiscale Residual Attention Network for Light Guide Plate Defect Detection

Aiming at the difference in density between the light guide point distribution of the light guide plate (LGP) images, the different size, shape and brightness of LGP defects, and the limitation of a small number of defect samples, our paper proposes a two-stage multiscale residual attention network based on “segmentation + decision” for LGP defect detection. In the first stage, a segmentation subnet is constructed to achieve accurate location of multiscale defects by using the U-shaped structure and the designed Multiscale Residual Attention Unit (MRAU). In the second stage, a decision subnet is constructed to achieve accurate decision whether the LGP image is defective under the guidance of features extracted by segmentation subnet. The design of the network enables the model to be trained using a small number of samples, which is a significant requirement for practical applications. Finally, a lot of experimental studies were conducted on two datasets, including compared with related deep learning methods, proving the effectiveness of residual attention mechanism and proving the effectiveness of multiscale mechanism. Experimental results show that the detection method in this paper has advantages in both detection accuracy and universality under the limitation of a small number of samples. In particular, the detection indicator F1-Score respectively reached 99.85% and 95.46% on the two datasets. Combined with an image acquisition platform, a surface defect detection system is implemented to realize the real-time production quality inspection on the production lines.