| Citation: | ZHAO Hongmei, PENG Bo, ZHOU Zhihong, et al. Automatic extraction method of air bubbles in icing microscopic images[J]. Journal of Aerospace Power, 2023, 38（11）：2738-2746 doi: 10.13224/j.cnki.jasp.20220217
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In view of the problems of high missed detection rate and inability to separate adhering bubbles in the extraction of icing microscopic images by traditional image segmentation methods, a method combining deep neural network and traditional segmentation algorithm was proposed. Based on the attention U-Net network, a two-branch fusion prediction strategy was adopted to extract the bubbles in the icing microscopic images. For some bubble adhesion problems, histogram equalization and local minima were introduced, and a watershed algorithm based on distance transformation was used to segment the adhesion bubbles in icing microscopic images twice. The experimental results showed that the two-branch prediction prediction Attention U-Net network was more accurate for the extraction of bubbles in different icing microscopic images, especially the detection rate for smaller bubbles was higher. The pixel accuracy, mean pixel accuracy, mean inter-section over union and frequency weighted intersection over union of the test images reached 0.9767, 0.8916, 0.8188 and 0.9575, respectively. The watershed algorithm based on distance transformation also showed good performance in the segmentation of sticky bubbles, providing quantifiable data support for the subsequent statistics of the number and area of bubbles.
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