Automatic Bridge Crack Detection Using a Convolutional Neural Network

Abstract

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

“…From Table 6, it is noticed that the proposed crack detection and severity recognition system outperformed the traditional classification, which doesn't apply feature selection, for the 3 crack detection datasets (1, 2, 4). Whereas, it increased the detection rate by approximately 27%, 27%, and 57% for bridge-cracks-dataset [39], our-dataset-2, and our-dataset-1, respectively. It also reduced the features by approximately 64.68%-68.8% for all cases.…”

“…The proposed system obtains Fmeasure ratio of 96.86% using hand-crafted features. Compared to the systems proposed in [6], [32], [34], [35], [37], [39] based on CNN deep learning models, the proposed system outperforms all of them in terms of accuracy. The proposed system achieves an accuracy of 96.86% for crack detection datasets.…”

“…For bridge-cracks-dataset [39], it was observed that the obtained accuracy was 93.46%. Moreover, the achieved recall percentage was 85.37% as a result of only 1115.8 TP hits out of 1169 actual crack images, as shown in Figure 9 (III).…”

“…In Table 7, the performance of the proposed system is illustrated considering our-dataset-1, our-dataset-2, and bridgecracks-dataset [39]. For our-dataset-1, it was noticed that the achieved accuracies were 96.27% and 89.82% for internal and external testing datasets, respectively.…”

“…In [39], Xu et al presented an end-to-end CNN-based crack detection model to improve the crack detection accuracy via avoiding losing the information of crack edge caused by the process of pooling. The proposed model has the advantage of combining the power of the atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module, and depth-wise separable convolution in obtaining denser feature map, and multi-scale image feature with avoiding details loss and reducing computational complexity.…”