Abstract
Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance.
Highlights
In modern society, it is important to ensure the safety of bridges
In order to fairly test the performance of Skip-Squeeze-and-Excitation Networks (SSENets), we choose to compare with the model proposed by Xu et al [25] and several traditional classification models for comparison
Considering the great improvement in the specificity factor, which is shown in Table 7, we conclude that SSENets can reduce the proportion of background images that are classified as crack images
Summary
Crack is one of the most common diseases of bridge structures, so detecting and repairing cracks in time are important tasks for the maintenance of bridges [1]. It can effectively prevent bridge quality problems from endangering transportation safety. In view of the strict requirements for bridge safety, we have to detect tiny cracks successfully and overcome the interference of noise, scratches and uneven illumination to the detection results. With an advancement in computer vision and deep learning techniques, computer vision has been applied in the field of crack detection [2,3], solving the problem of crack detection methods in recent decades
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