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基于改进YOLOv5的电子元器件实例分割算法

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为解决电子元器件组装中不同类型电子元器件难以自动区分的问题,利用改进的YOLOv5网络对电子元器件进行了实例分割,实现了不同元器件的自动识别分类.首先,使用三通道直方图均衡化对图像进行预处理.其次,在不增加模型复杂度的前提下,使用SE-Net通道注意力模块增强网络的特征提取能力,压缩无用信息;利用GhostNet实现网络轻量化;采用BiFPN增强网络特征融合能力.实验得出,采用改进的YOLOv5方法对电子元器件实例分割,其mAP为96.7％,单图的检测时间平均为45.5 ms.试样结果表明,该实例分割方法优于同类方法,对提高电子元件的自动化检测水平具有实用意义.
Instance segmentation algorithm of electronic components based on improved YOLOv5
To address the challenge of automatic recognition of electronic components on an assembly line, an improved YOLOv5 was used to implement instance segmentation of four categories of electronic components. Firstly, multi-channel histogram equalization was used for image preprocessing. Then, the YOLOv5 was improved: Segmentation head was added; Sequeeze-and-excitation net(SE-Net) channel attention module was embedded to enhance the feature extraction capability and to compress the useless information without increasing the model complexity; GhostNet was used to make the model lightweight; and BiFPN was used to enhance model feature fusion capability. Finally, experimental results showed that the mAP of the proposed method could reach 96.7％ and the detection time of a single image was 45.5 ms. The results prove that proposed method has superior performance than that based on mask region-based conventional neural network(Mask RCNN) and initial YOLOv5, and has practical significance for automatic detection of electronic components.

instance segmentationdeep learningYOLOv5components detection

杨祎宁、魏鸿磊

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大连工业大学 机械工程与自动化学院,辽宁大连 116000

实例分割 深度学习 YOLOv5 元器件识别

2021 General Project Scientific Research Funds of Education Department of Liaoning Province2021 General Project Scientific Research Funds of Education Department of Liaoning Province

LJKZ0535LJKZ0526

2024

10.62756/jmsi.1674-8042.2024003

测试科学与仪器
中北大学

测试科学与仪器

影响因子：0.111
ISSN：1674-8042
年,卷(期)：2024.15(1)
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