用YOLOv8实现光学镜片缺陷的高效自动检测。
YOLO Network For Defect Detection In Optical lenses
- 基于YOLOv8构建缺陷检测模型，支持实时识别。
- 自建镜片缺陷数据集，标注缺陷与镜片区域。
- 适合工业质检场景，可扩展性强。
大批量生产的光学镜片常存在影响散射特性的缺陷，危及质量标准。人工检测准确率低、错误率高且难以规模化，不具可行性。为此，本文提出一种基于YOLOv8深度学习模型的自动化缺陷检测系统。研究构建了一个自定义的光学镜片数据集，对缺陷区域与镜片本体进行标注以训练模型。实验结果表明，该系统能高效、准确地检测光学镜片缺陷，适用于实时工业环境，可显著提升镜片制造过程中的质量控制水平，实现可靠且可扩展的缺陷检测。
原文摘要 · Abstract (English)
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.
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