This study presents a real-time edge-based inspection system for detecting subtle defects including black spot defects, discoloration, and short shots in transparent optical molded components. A multi-source imaging platform was established, and a full-factorial design was conducted to evaluate the effects of illumination wavelength, imaging mode, and cropping scale on defect visualization. Brightness equalization, gamma correction, and multi-scale cropping were applied to enhance feature visibility, while rotation, flipping, and brightness perturbation were used for data augmentation. A grid-search strategy optimized the YOLOv8 model across two optimizers, learning rates, and input resolutions, achieving an mAP 50–95 exceeding 0.93 for all defect categories. The optimized model was deployed on an edge device integrated with a camera, controlled lighting, and a motorized linear stage, enabling automated, real-time inspection. Compared with manual inspection, the proposed system reduces inspection time from 36 to 3.5 s, an improvement of roughly 92%, while maintaining comparable accuracy. The results demonstrate that the strategic integration of multi-source imaging and targeted image preprocessing enables deep learning architectures to effectively overcome the inherent challenges of low contrast and high reflectance in transparent materials. The primary contribution of this study lies in the successful identification of extremely subtle defects, thereby providing a highly stable and practical edge-computing inspection framework for smart manufacturing environments.
Ke et al. (Fri,) studied this question.