Automatic segmentation of airport pavement damage by AM‐Mask R‐CNN algorithm

Abstract

The airport is an important infrastructure for air transport and urban traffic. The airport pavement damage seriously affects the safety of aircraft take‐off and landing. Therefore, the regular detection of airport pavement damage is critical for aircraft take‐off and landing safety. However, for small target areas, the existing pavement damage detection methods cannot effectively achieve detection. In addition, the airport pavement detection is carried out under low light conditions at night, and the existing detection methods may not be able to accurately detect the damage boundary. To address the above problems, an automatic detection algorithm of Mask R‐CNN algorithm integrating attention mechanism (AM‐Mask R‐CNN) for airport pavement damage is proposed. First, the AM‐Mask R‐CNN is developed based on an improved Mask R‐CNN modified by the feature pyramid (FPN) and the dual attention mechanism are fused to extract subtle features in images. In addition, feature fusion is used. The macroscopic and microscopic features of airport pavement damage are organically combined to improve the segmentation sensitivity of small target areas and dark lighting. The experimental results show that the average F1‐score of the proposed model is 0.9489. In addition, the mean intersection over union for the model proposed is 0.9388. The average segmentation speed can reach 11.8 FPS. Moreover, compared with the traditional threshold segmentation method Fully Convolutional Networks (FCN) and DeepCrack segmentation method, the effectiveness of the proposed method is further proved.

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