@Article{cmc.2023.042582,
AUTHOR = {Lisang Liu, Chengyang Ke, He Lin},
TITLE = {Mobile-Deep Based PCB Image Segmentation Algorithm Research},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2443--2461},
URL = {http://www.techscience.com/cmc/v77n2/54821},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.042582}
}