| 贾琦,王国磊,路敦民.基于GCSS-YOLOv8的涂层表面微小缺陷检测[J].电子测量与仪器学报,2026,40(6):116-125 |
| 基于GCSS-YOLOv8的涂层表面微小缺陷检测 |
| GCSS-YOLOv8-based tiny defect detection on coating surfaces |
| |
| DOI： |
| 中文关键词: 涂层表面质量检测 多尺度缺陷 图像预处理 GCSS-YOLOv8 |
| 英文关键词:coating surface quality inspection multi-scale defects image preprocessing GCSS-YOLOv8 |
| 基金项目:北京市自然科学基金（3252004）项目资助 |
|
|
|
|
| 摘要点击次数: 66 |
| 全文下载次数: 28 |
| 中文摘要: |
| 涂层表面微小缺陷的自动检测在制造业质量控制中具有重要意义。针对大尺寸飞机壁板表面涂层质量检测的关键需求，提出一种基于机器视觉的多尺度缺陷协同检测方法，重点解决毫米级麻点、鱼眼、气泡与厘米级划痕、流挂等跨尺度缺陷的同步识别难题。首先系统分析了现有检测方法在多尺度缺陷同步识别，以及实时性和高精度之间平衡方面的不足，并在此基础上展开研究。完成的核心内容包括构建了多光源协同图像采集平台，建立覆盖5类典型缺陷的涂层图像数据集；设计了一种融合非线性光照校正与多尺度增强的复合图像预处理方法，有效解决了由局部反光及明暗突变引起的亮度梯度异常问题；提出了改进的GCSS-YOLOv8深度学习模型，以提升对不同尺度缺陷的检测性能。实验结果表明，所提出方法在自建数据集上的平均检测精度（mAP@0.5）达到91.4%，较原始YOLOv8模型提升1.9%，验证了其有效性与实用价值。 |
| 英文摘要: |
| Automatic detection of tiny defects on coating surfaces holds significant importance in manufacturing quality control. Addressing the critical need for quality inspection of coating surfaces on large-scale aircraft panels, this paper proposes a machine vision-based multi-scale defect collaborative detection method, focusing on solving the challenge of synchronously identifying cross-scale defects including millimeter-scale pits, fisheyes, and bubbles as well as centimeter-scale scratches and sags. This paper first systematically analyzed the shortcomings of existing detection methods in terms of synchronous recognition of multi-scale defects and balancing real-time performance with high precision, and conducted research accordingly. The core contributions completed include: constructed a multi-light source collaborative image acquisition platform and established a coating image dataset covering five typical defect types; designed a composite image preprocessing method that integrates nonlinear illumination correction and multi-scale enhancement, effectively addressing brightness gradient anomalies caused by local reflections and sudden brightness changes; and proposed an improved GCSS-YOLOv8 deep learning model to enhance detection performance for defects of different scales. Experimental results show that the proposed method achieves a mean Average Precision (mAP@0.5) of 91.4% on the self-built dataset, which is 1.9% higher than the original YOLOv8 model, verifying its effectiveness and practical value. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
