YOLO Network For Defect Detection In Optical lenses
This addresses quality control issues in optical lens manufacturing by providing a scalable alternative to manual inspection, though it is incremental as it applies an existing method to a new domain.
The study tackled the problem of detecting defects in mass-produced optical lenses by developing an automated system using the YOLOv8 deep learning model, achieving efficient and accurate detection suitable for real-time industrial use.
Mass-produced optical lenses often exhibit defects that alter their scattering properties and compromise quality standards. Manual inspection is usually adopted to detect defects, but it is not recommended due to low accuracy, high error rate and limited scalability. To address these challenges, this study presents an automated defect detection system based on the YOLOv8 deep learning model. A custom dataset of optical lenses, annotated with defect and lens regions, was created to train the model. Experimental results obtained in this study reveal that the system can be used to efficiently and accurately detect defects in optical lenses. The proposed system can be utilized in real-time industrial environments to enhance quality control processes by enabling reliable and scalable defect detection in optical lens manufacturing.