Accurate segmentation of infrared images for circuit board diagnosis using an improved Deeplabv3+ network

Frontiers in Physics

Physics

Jianxin Hao¹  Xuan Xia²  Meng Zhu²  Zhixin Li²  Li Wang³ 

[1] Engineering Techniques Training Center, Civil Aviation University of China, Tianjin, China;Institution of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China;Vocational and Technical College, Civil Aviation University of China, Tianjin, China;

关键词: infrared image segmentation;    DeepLabV3+;    MobileNetV2;    CBAM;    composite loss function;   

DOI  :  10.3389/fphy.2023.1223548

 received in 2023-05-16, accepted in 2023-06-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

An effective infrared image segmentation algorithm is essential for non-contact fault diagnosis of circuit boards. However, the uneven grayscale of the infrared images, multiple target regions, and large radiation noise pose challenges to achieving accurate segmentation and efficient data extraction for the interested regions. In this paper, we propose a segmentation algorithm based on the Deeplabv3+ network, using the lightweight MobileNetV2 as a replacement for the original Xception backbone network to improve computational efficiency and reduce overfitting. We also employ a composite loss function and cosine annealing learning rate to balance foreground-background segmentation and avoid local optima. Furthermore, we integrate the Convolutional Block Attention Module (CBAM) to extract and combine important spatial and channel features, allowing the algorithm to focus on identifying elements of the circuit board instead of background pixels, thereby improving segmentation accuracy. Experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance in terms of both segmentation accuracy and computational efficiency on our self-built infrared circuit board dataset, with a MIoU of 90.34%, MPA of 95.26%, and processing speed of 25.19 fps. Overall, our proposed segmentation algorithm can effectively identify the key regions of interest in infrared images of circuit boards, providing technical support for non-contact diagnosis.

【 授权许可】

Unknown   
Copyright © 2023 Hao, Xia, Wang, Li and Zhu.