Development and Application of Convolutional Neural Networks in Industrial Defect Detection

This paper provides a comprehensive review of CNN research in industrial defect detection. It first traces the technological evolution of CNNs, from the early biologically inspired Neocognitron to modern efficient architectures (such as ResNet and MobileNet), and analyzes their core advantages in feature extraction and model generalization. Then, in response to common challenges in industrial scenarios, including scarce defect samples, complex background interference, and high real-time requirements, it summarizes key strategies such as data augmentation, model lightweighting, and multimodal fusion. Furthermore, by examining typical application scenarios like electronics manufacturing, metal processing, and semiconductor wafer inspection, the paper analyzes the technological innovations and practical achievements of CNN deployment. Finally, it forecasts future trends, covering areas such as edge intelligence deployment, enhanced interpretability, and multi-task adaptive learning. The study demonstrates that CNNs are evolving from mere detection tools into core technologies for intelligent quality control. Their deep integration with industrial scenarios will continue to drive the digital transformation of the manufacturing industry.