基于YOLO的PCB表面缺陷的轻量级检测算法
Shiwei Yu1, Feng Pan1, Xiaoqiang Zhang1
1CGN Digital Technology Co., Ltd., Shanghai, China.
PloS one
|April 18, 2025
概括
本研究介绍了一种基于YOLO的轻量级PCB缺陷检测算法,显著减少计算负载和模型大小,同时提高准确性. 增强的模型是理想的部署在资源有限的平台.
科学领域:
- 电子 电子 电子 电子 电子 电子 电子
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 印刷电路板 (PCB) 缺陷检测面临着低准确度和高计算要求的挑战.
- 现有的算法经常在混乱的背景和微妙的缺陷变化中扎.
研究的目的:
- 开发一种轻量级且准确的PCB缺陷检测算法.
- 优化模型,以便在低算术性平台上有效部署.
主要方法:
- 在脊柱中利用了GhostNet来实现模型的轻量化.
- 改进了子网络,使用深度可分离的卷积来减少参数.
- 集成的Swin转换器与C3模块 (C3STR) 处理复杂的图像背景和缺陷类型.
- 用双向特征金字塔网络 (BIFPN) 取代PANet,以增强多尺度特征融合.
主要成果:
- 实现了参数数量的47.2%的减少.
- 将GFLOP降低了48.5%,模型重量降低了42.4%.
- 平均平均精度 (mAP) 增加了2.4%,而每秒 (FPS) 仅下降了2.0%.
结论:
- 拟议的基于YOLO的轻量级算法为PCB缺陷检测提供了精度和计算效率之间的卓越平衡.
- 该模型的复杂性降低使其非常适合嵌入式系统和低算法平台上的实时应用.
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