| Citation: | LI Haifeng, HAN Hongyang. Algorithm to detect thin strip-shaped structural diseases on airport pavement in complex background[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 36-44. doi: 10.13700/j.bh.1001-5965.2020.0512(in Chinese)
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The structural diseases on airport pavement, such as cracks, corner fractures, broken seams, and repairs, have the characteristics of narrow width, different length, and less pixel proportion in the image, which show a thin strip-shaped structure, and the contrast is low in the complex background. These factors lead to detection failure when using the existing detection algorithms. To solve these problems, a deep neural network model, named as DetMSPNet, based on attention mechanism and feature fusion is proposed. First, the attention mechanism module CBAM is used to make the feature learning more focused on the disease area of the thin strip-shaped structure and suppress the interference information. Second, the residual atrous pyramid module is constructed to extract the feature information with different scales. Then, the maximum pooling branch is designed to facilitate the fusion of different features from shallow and deep layers, and improve the positioning ability of the model for diseases. In addition, the deep features are fed into three dilated convolutions with different dilated rates and pyramid pooling modules, so that the disease features contain more global context information. Finally, the disease features generated from all levels are fused to fulfil the information complementation from different scales and different levels. The comparative experiment was conducted with three classical object detection algorithms on APD dataset, and the results show that the proposed algorithm achieves an mAP of 78.51%, which is better than its counterparts. The proposed DetMSPNet improves the adaptability of the algorithm to the narrow width, different length, less proportion of pixels in the image and low contrast with complex background for the detection of thin strip-shaped structural diseases on airport pavement. The experimental results show that the average detection accuracy of the proposed algorithm is improved.
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