# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 1 Introduction  
Bridges play a significant role in daily life. Regular bridge checks are important for maintaining the structural health and reliability of bridges. Bridge crack is one of the main damages of bridges, and its detection is an important task for bridge maintenance. Traditional bridge detection methods rely on human visual inspection, so the detection efficiency and accuracy cannot be guaranteed. In recent years, machine learning and computer vision were applied to the field of crack detection [1; 2; 3; 4; 5], and achieved good results.  
The modern convolutional neural network (CNN) was first proposed by [PERSON] et al. [6] in 1989. Due to its effectiveness in feature extraction, it is widely used in computer vision tasks such as image classification [7; 8; 9; 10], object recognition [11; 12; 13], and action recognition [14; 15; 16].  
Inspired by these achievements, recent studies have applied convolutional neural networks to the area of crack detection. In 2004, [PERSON] et al. [17] introduced an algorithm, based on the standard genetic algorithm (GA), to automatically detect cracks, which had a crack detection accuracy of 92.3 \(\pm\) 1.4% for 100 images. Since the dataset was too small, the effectiveness of this method cannot be fully proven. In 2016, [PERSON] [18] et al. proposed an algorithm for classifying pavement cracks. However, since the algorithm did not have strict classification criteria for positive and negative samples, the detection accuracy was only 86.96%. In 2017, based on transfer learning, [PERSON] et al. [19] proposed a concrete crack classifier, where the Visual Geometry Group Network 16(VGG-16) model got the best performance. In the same year, an algorithm for analyzing cracks in a single video frame using a convolutional neural network was developed by [PERSON] et al. [20], combined with the Naive Bayes data fusion scheme to aggregate video information. The network trained by [PERSON] et al. [21], combined with the sliding window technique, could scan any particular crack image with a resolution larger than 256 \(\times\) 256 pixels. [PERSON] et al. [22] used CNN to detect pavement cracks and used principalcomponent analysis (PCA) to classify the detected pavement cracks. [PERSON] et al. [23] demonstrated the effectiveness of using deeper networks to improve the detection accuracy in computer vision-based pavement crack detection. However, these methods still have problems of low precision or high model complexity, so they are not widely used.  
In this article, we proposed an end-to-end model based on the convolutional neural network to detect bridge cracks automatically. Our main contributions are as follows:  
* We used a single CNN trained end-to-end with images to detect cracks, using only images and image labels as input. Our CNN model achieved a 96.37% accuracy in detecting cracks without pre-training and fine-tuning on other datasets.
* To the best of our knowledge, we introduced the Atrous Spatial Pyramid Pooling (ASPP) module into the field of crack detection for the first time, and achieved good experimental results.
* We introduced atrous convolution with an atrous rate of 2 in the last three convolutional layers of the network, replacing the maxpooling layer, and thus avoiding the loss of crack edge information caused by pooling. We proved the effectiveness of replacing the maxpooling layer with atrous convolution through experiments.