We proposed a modified concrete bridge crack detector based on a deep learning-assisted image processing approach. Data augmentation technology is introduced to extend the limited dataset. In our proposed method, the bounding box for the crack is detected by YOLOv5. Then, the image covered by the bounding box is processed by the image processing techniques. Compared with the conventional image processing-based crack detection method, the deep learning-assisted image processing approach leads to higher detection accuracy and lower computation cost. More precisely, the mask filter is employed to remove handwritten marks, and the ratio filter is adopted to eliminate speckle linear noises. When a single crack is detected by several bounding boxes, we proposed a novel fusion method to merge these bounding boxes. Furthermore, we proposed a connected component search approach based on the crack trend of the area to improve the connection accuracy. With the crack detector, the cracks that are wider than 0.15 mm can be correctly detected, quantified, and visualized. The detection absolute error of the crack width is less than 0.05 mm. Thus, we realized fast and precise detection and quantification of bridge crack based on the practical engineering dataset.

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We propose a deep convolutional spiking neural network (DCSNN) with direct training to classify concrete bridge damage in a real engineering environment. The leaky-integrate-and-fire (LIF) neuron model is employed in our DCSNN that is similar to VGG. Poisson encoding and convolution encoding strategies are considered. The gradient surrogate method is introduced to realize the supervised training for the DCSNN. In addition, we have examined the effect of observation time step on the network performance. The testing performance for two different spike encoding strategies are compared. The results show that the DCSNN using gradient surrogate method can achieve a performance of 97.83%, which is comparable to traditional CNN. We also present a comparison with STDP-based unsupervised learning and a converted algorithm, and the proposed DCSNN is proved to have the best performance. To demonstrate the generalization performance of the model, we also use a public dataset for comparison. This work paves the way for the practical engineering applications of the deep SNNs.