Casting Defect Detection and Recognition Technology Based on Deep Learning - Song, Ziqing; Yao, Yupeng; Yang, Jingbin (International Journal of Computer Science and Information Technology, 2025)
Aiming at the existing casting defect detection and recognition technology based on deep learning, the limitations of traditional manual detection methods and the advantages of machine vision technology in improving detection accuracy and efficiency are discussed. The research status of casting defect detection is analyzed, including surface defect and internal defect detection technology, as well as image preprocessing, algorithm framework optimization and design innovation direction. By reviewing a variety of deep learning models and algorithms, such as CNN, transfer learning, lightweight models, and improved U-Net models, the potential of these techniques in improving the efficiency and accuracy of defect detection is demonstrated. In addition, this paper discusses the three key modules of visual defect detection technology: image acquisition, image preprocessing and image analysis, and compares the characteristics of one-stage and two-stage object detection algorithms. Finally, the main research directions of casting defect detection are summarized to provide directions for future research.