| Citation: | CAO S Y，LIU J Q，SONG G T，et al. Borehole image detection of aero-engine based on self-attention semantic segmentation model[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（6）：1504-1515 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0448
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Aiming at the problems that small-scale faults tend to be missed and misjudged when detecting borehole images of aero-engines by using traditional methods, a new method based on self-attention semantic segmentation (SA-SS) model is proposed. Based on the overall architecture of classical semantic segmentation model DeepLabv3+, a lightweight MobileNetV2 is adopted as the backbone feature extraction network instead of Xception to reduce calculation by utilizing expansion-extraction-compression strategy; based on the idea of multi-layer cascade, original atrous spatial pyramid pooling structure of DeepLabv3+ is improved to keep more feature information in feature map; a self-attention mechanism is fused to establish the internal correlation of global pixels and strengthen the attention to details. The decoding layer of original DeepLabv3+ is improved; multi-scale spatial fusion method is introduced into low-level feature extraction to fuse multiple layers of features for classification. Experimental results show that compared with original DeepLabv3+, SegNet-ResNet and other methods, mean intersection over union and pixel accuracy and PA of SA-SS are increased by 4.10% and 3.92% respectively. Also, training cost and detection speed are improved by 24.43% and 5.11frame/s respectively.
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