A Multitask Learning-Based Neural Network for Defect Detection on Textured Surfaces Under Weak Supervision

Abstract

Automatic defect detection on textured surfaces has long been one of the hotspots in the computer vision community. Many methods based on deep learning (DL) have been proposed in the past few years although most are semantic segmentation-based methods that require a large number of accurately labeled samples. However, obtaining precise labels is time-consuming and costly. In this article, we propose a novel weakly supervised DL-based method to accurately segment and locate defects on textured surfaces. In the proposed method, we design one encoder for extracting features and two decoders for two related tasks. To ensure that the deep neural network benefits from multitask learning, the two decoders share the unique encoder. The main task is designed to restore the defects on textured surfaces, and the auxiliary task is designed to obtain the region of interest (ROI), which is employed to filter out noise in the main task. Subsequently, we can obtain residual maps by comparing the original images and the restored images. Finally, more accurate results can be obtained through the fusion of residual maps and ROIs. A series of experiments on the public defect detection dataset DAGM showed that our method demonstrates the best performance compared with other state-of-the-art methods.