This paper presents an enhanced Faster R-CNN model for detecting surface defects in resistance welding spots, improving both efficiency and accuracy for body-in-white quality monitoring. Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using the SmoothL1 loss function, and applying Fast R-CNN to classify detected defects. Additionally, a new pruning model is introduced, reducing unnecessary layers and parameters in the neural network, leading to faster processing times without sacrificing accuracy. Tests show that the model achieves over 90% accuracy and recall, processing each image in about 15 ms, meeting industrial requirements for welding spot inspection.

Dunhuang murals are an important part of human cultural heritage with extraordinary literary and historical value. However, with the influences of time and the environment, many murals have suffered large defects and require complete reconstructions of their historical information, which is difficult. Moreover, the manual restoration of murals is complicated and slow. We attempted to use deep learning techniques to restore two types of damaged murals, including central regular defects and random irregular defects. First, we created a dataset of 26,240 Dunhuang murals for model training. Second, we built an image restoration model based on U-net to automatically restore murals. We show that mapping global information to defective areas can effectively restore murals with random irregular defects. Compared to manual restoration, the proposed model significantly reduces the time and cost of restoration work. In addition, we propose a method for constructing and preserving the contour maps of mural images and present the model’s training process. Using the deep learning model, professional restorers can provide visual feedback on the restoration process to directly compare the restoration patterns of different rounds. This method implements observations of the filling-in process in the defective areas. Finally, we built a Dunhuang mural restoration system for professional restorers to restore, select, and share murals on an online platform.

The deep learning architecture YOLO (You Only Look Once) has demonstrated its superior visual detection performance in various computer vision tasks and has been widely applied in the field of automatic surface defect detection. In this paper, we propose a lightweight YOLOv8-based method for the quality inspection of car body welding spots. We developed a TA-YOLOv8 network structure which has an improved Task-Aligned (TA) head detection, designed to handle a small sample size, imbalanced positive and negative samples, and high-noise characteristics of Body-in-White welding spot data. By learning with fewer parameters, the model achieves more efficient and accurate classification. Additionally, our algorithm framework can perform anomaly segmentation and classification on our open-world raw datasets obtained from actual production environments. The experimental results show that the lightweight module improves the processing speed by an average of 2.8%, with increases in detection the mAP@50-95 and recall rate of 1.35% and 0.1226, respectively.

Welding spot defect detection using deep learning methods provides an effective way of body-in-white quality monitoring. Based on the existing Faster R-CNN model, this paper proposed an improved faster R-CNN model for resistance welding spot surface defect inspection to improve inspection efficiency and accuracy. The model contains the following improvements. Firstly, the improved algorithm uses anchor box with higher confidence output by the RPN network to locate welding spots. When a defect is detected and the detection system is in a suspended state, the Fast R-CNN network is used to confirm the defect category and details. Secondly, a new pruning model is proposed to replace the entire backbone neural network, which unnecessary convolutional layers and connection layers are deleted, and some parameters of each hidden layer are further reduced. On the premise of ensuring detection accuracy, the parameter quantity is extremely reduced, and the speed is improved. Experiments show that the model proposed in this paper took about 15ms for one single image test, and both the detection accuracy and recall rate reached over 90% according to the test on our dataset. This deep learning model meets the requirements of welding spot defect detection.