Applications and Development of Deep Learning with Persistent Homology in Industrial Defect Detection

Addressing the industry pain points of traditional convolutional neural networks and standard hybrid deep learning architecture-based industrial defect detection methods, which only extract pixel-level features and fail to capture the inherent topological geometric structures of defects such as cracks, holes, and circular scratches, and where minor defects are easily masked by noise and cross-condition detection accuracy significantly declines, this paper systematically reviews the theoretical system, complete detection process, and practical application results of the integration of persistent homology and deep learning. It summarizes five core challenges faced by current technologies: scarcity of defect samples, weak dynamic adaptability, difficulty in balancing lightweight and detection accuracy, complex background interference, and insufficient generalization ability. Finally, from five dimensions including digital twin sample generation, industrial topology pre-training, lightweight topology operator end-cloud collaboration, interpretable multimodal industrial large model, and unified general detection architecture, this paper proposes future technological development directions for the industry. The aim is to provide a new topological technology route for fully automated intelligent quality inspection in smart manufacturing, and to offer theoretical and engineering references for the research and development of intelligent detection equipment and algorithm iteration.