全球上下文注意力增强了YOLO的ConvMixer预测头,用于PCB表面缺陷检测
Kewen Xia1, Zhongliang Lv2, Kang Liu1
1School of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Scientific reports
|June 16, 2023
概括
本研究介绍了GCC-YOLO,这是一个增强的YOLO模型,用于印刷电路板 (PCB) 检查. 它通过使用全球上下文注意力和ConvMixer预测头来改善小组件的检测,从而提高准确性和更快的处理速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 印刷电路板 (PCB) 检查面临的挑战是检测许多小目标和复杂的背景纹理,导致错过和错误的检测.
- 现有的物体检测模型在精确的PCB缺陷识别所需的细粒度细节方面扎.
研究的目的:
- 开发一个先进的物体检测模型,GCC-YOLO,专门设计用于提高PCB检查中小目标的检测.
- 为了提高PCB缺陷检测的精度,回忆和整体准确性,同时保持计算效率.
主要方法:
- 提出了一个全球上下文注意力增强的YOLO模型与ConvMixer预测头 (GCC-YOLO).
- 使用高分辨率特征层 (P2) 来增强小目标的细节和位置信息.
- 集成了一个全局上下文注意模块 (GC) 与脊柱中的C3模块,用于抑制噪音和功能增强.
- 采用双向加权特征金字塔 (BiFPN) 以有效的特征融合和减少信息丢失.
- 引入了一个与C3模块相结合的ConvMixer模块,用于新型预测头,以改善小目标检测和减少参数.
主要成果:
- 在PCB数据集上,GCC-YOLO显示了与YOLOv5s相比显著的改进,精度增加了0.2%,回忆增加了1.8%,mAP@0.5增加了0.5%,mAP@0.5:0.95增加了8.3%.
- 与其他最先进的算法相比,拟议的模型实现了较小的模型体积和更快的推理速度.
- 证实了检测小目标和抑制背景噪音的增强能力.
结论:
- GCC-YOLO有效地解决了在PCB检查中检测小目标和复杂背景的局限性.
- 全球上下文注意力和ConvMixer预测头的整合提供了一种有前途的方法,用于提高专门的工业应用中的对象检测性能.
- 该模型在检测精度和计算效率之间提供了平衡,使其适合于现实世界PCB制造质量控制.
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