Open Access

ARTICLE

Mobile-Deep Based PCB Image Segmentation Algorithm Research

Lisang Liu¹, Chengyang Ke^1,*, 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:

Computers, Materials & Continua 2023, 77(2), 2443-2461. https://doi.org/10.32604/cmc.2023.042582

Received 05 June 2023; Accepted 29 August 2023; Issue published 29 November 2023

Abstract

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.

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Keywords

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