@article{YANG2024, 
author = {Yining YANG and Honglei WEI},
title = {Instance segmentation algorithm of electronic components based on improved YOLOv5},
year = {2024},
journal = {Journal of Measurement Science and Instrumentation},
volume = {15},
number = {1},
pages = {23-32},
keywords = {instance segmentation, deep learning, YOLOv5, components detection},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2024003},
doi = {10.62756/jmsi.1674-8042.2024003},
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.}
}