Spatiotemporal defect detection using convolutional recurrent network and detection transformer

Abstract

Single image-based defect detection can be widely used in several production lines. Depending on the performance of these defect detections, accurate detection of defects is essential because many lives and property damage can occur. However, existing defect detection algorithms detect defects based on a single image. Therefore, it is vulnerable to noise generated in the image due to poor environment, such as vibration noise can be generated while capturing the image in the actual field. In addition, to train a deep learning network, pixel-level annotation is generally required. However, obtaining pixel-level annotation costs a lot of cost and time. Therefore, we propose a welding defect detection method that processes spatio-temporal data using a Convolutional Recurrent Reconstructive Network (CRRN). In addition, for the efficiency of defect data annotation, we propose a weakly supervised defect detection method. First, for an environment where pixel-level labeling data can be acquired, we design a bi-directional recurrent reconstructive network (bi-CRRN) that detects defects based on supervised learning based on spatio-temporal data. In addition, we propose a spatio-temporal deformable detection transformer (STD-DETR) that can perform welding defect detection by supervised learning method, which only needs a frame-level label for training, based on spatio-temporal data. To verify the proposed method, a dataset is generated by capturing a welding bead on an actual ship using a vision camera. We also demonstrate the superiority of defect detection performance by applying the proposed method to the generated dataset.