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一个基于轻量级小型物体检测头的新型YOLOv5_ES用于PCB表面缺陷检测

Yi Gao¹, Zhensong Li², Yutong Wang¹

¹Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China.

Scientific reports

|October 9, 2024

概括

本研究引入了改进的YOLOv5模型用于印刷电路板 (PCB) 缺陷检测,提高了对小缺陷的准确性和效率. 新方法显著降低了模型尺寸和计算成本,同时提高了缺陷识别能力.

科学领域:

* * 制造业工程制造业工程
* 计算机视觉计算机视觉
* 人工智能 * 人工智能

背景情况:

*印刷电路板 (PCB) 制造中的表面缺陷极大地影响产品质量.
*传统的对象检测算法在PCB检查中与复杂的背景,多种类型的缺陷和小型缺陷作斗争.
*现有的方法往往缺乏现实世界PCB制造环境所需的准确性和效率.

研究的目的:

* 开发一个高精度和高效的PCB缺陷检测算法,以改善制造质量控制.
* 解决传统物体检测方法在识别PCB上的小型和复杂缺陷方面的局限性.
*为实时PCB表面检查创建一个轻量级但功能强大的模型.

主要方法:

* 提出了一种新的YOLOv5缺陷检测算法 (YOLOv5_ES),该算法结合了多尺度注意力机制 (EMA) 和空间金字塔扩展卷积 (SPD-Conv).
*优化了YOLOv5s框架,修改了检测头以关注小检测层,提高了轻微缺陷的识别.
* 集成的SPD-Conv可减少特征提取过程中的信息丢失,EMA可融合多层次的上下文信息,改善概括性.

主要成果:

*与标准YOLOv5s模型相比,平均平均精度 (mAP0.5) 提高了3.1%.
* 模型参数减少了55.8%,从而实现了更轻便和更高效的模型.
* 每秒千兆浮点运算 (GFLOPs) 减少4.8%,表明计算成本降低.

结论:

* 拟议的YOLOv5_ES模型在PCB缺陷检测方面表现出卓越的性能,特别是在小型和复杂的缺陷方面.
* 模型参数和计算负载的显著减少可以实现,而不会影响准确性.
* 增强型号为制造业中准确,高效和轻量级的PCB表面检查提供了有前途的解决方案.

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