Accurate segmentation of infrared images for circuit board diagnosis using an improved Deeplabv3+ network - Hao, Jianxin; Xia, Xuan; Wang, Li; Li, Zhixin; Zhu, Meng (Frontiers in Physics, 2023)
A 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 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 use a composite loss function and cosine annealing learning rate to balance foreground-background segmentation and avoid local optima. Furthermore, we add the the Convolutional Block Attention Module (CBAM) to extract and combine important spatial and channel features, which allows the algorithm to focus on identifying elements of the circuit board instead of background pixels and thus improve 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/s. Overall, our proposed segmentation algorithm can effectively identify the key regions of interest in infrared images of circuit boards, thus providing technical support for non-contact diagnosis.