Instance segmentation algorithm of electronic components based on improved YOLOv5

Abstract

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.

Keywords

instance segmentation / deep learning / YOLOv5 / components detection

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