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Mobile-Deep Based PCB Image Segmentation Algorithm Research

Lisang Liu1, Chengyang Ke1,*, He Lin2

1 School of Electronic Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China
2 State Grid Fujian Power Supply Co., Ltd., Xiapu Power Supply Company, Ningde, 355100, China

* Corresponding Author: Chengyang Ke. Email: email

Computers, Materials & Continua 2023, 77(2), 2443-2461.


Aiming at the problems of inaccurate edge segmentation, the hole phenomenon of segmenting large-scale targets, and the slow segmentation speed of printed circuit boards (PCB) in the image segmentation process, a PCB image segmentation model Mobile-Deep based on DeepLabv3+ semantic segmentation framework is proposed. Firstly, the DeepLabv3+ feature extraction network is replaced by the lightweight model MobileNetv2, which effectively reduces the number of model parameters; secondly, for the problem of positive and negative sample imbalance, a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability; in addition, a more efficient atrous spatial pyramid pooling (E-ASPP) module is proposed. In addition, a more efficient E-ASPP module is proposed, and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy; finally, the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach. The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%, a precision of 94.87%, a recall of 93.65%, and a balance score of 93.64% on the PCB test set, which is more accurate than the common segmentation algorithms Hrnetv2, UNet, PSPNet, and PCBSegClassNet, and the segmentation speed is faster.


Cite This Article

L. Liu, C. Ke and H. Lin, "Mobile-deep based pcb image segmentation algorithm research," Computers, Materials & Continua, vol. 77, no.2, pp. 2443–2461, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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