In order to improve the accuracy of surface defect detection for curved optical glass lenses , automated surface defect detection technology is studied. By analyzing the imaging principles of different defects on the surface of optical glass lenses , a defect collection device combining two lighting methods is designed to capture high contrast de- fect images while compensating for the shortcomings of optical lens defect detection in detachment defects;Preprocess and enhance the collected defect images to provide high-quality images for automated defect detection of optical glass lens;Applying deep learning methods to optical lens defect detection , by comparing the performance of different net- work models on the optical lens defect dataset ,Select the YOLOv5s with the best performance to complete the detection of lens defects , with a recall rate and average accuracy of 92% and 95% , respectively. The time to detect a defective lens is 10 ms.