YOLO Network For Defect Detection In Optical lenses

TL;DR

This paper introduces an automated defect detection system for optical lenses using the YOLOv8 deep learning model, demonstrating high accuracy and efficiency suitable for real-time industrial quality control.

Contribution

It presents a novel application of YOLOv8 for optical lens defect detection with a custom dataset and demonstrates its effectiveness in industrial settings.

Findings

High detection accuracy achieved

Real-time processing capability demonstrated

System improves quality control efficiency

Abstract

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.