# Bridge Crack Detection Based on SSENets
## 1 Introduction  
In modern society, it is important to ensure the safety of bridges. 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. Workers used to rely on subjective judgment to detect bridge cracks, which would cause the problems of low efficiency, accuracy and be time consuming, thus it is not appropriate for actual application. 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. In recent years, crack detection algorithms based on computer vision are being continuously developed. Threshold segmentation [4], morphological [5], wavelet transform [6], and the filter-based algorithm [7] have been applied to detect cracks. Although these algorithms may achieve high detection accuracy after adjusting parameters, they are only effective for images captured in specific environments. In other words, when the illumination and shooting distance change, the parameters need to be adjusted to ensure the high detection accuracy. To satisfy the requirement of working in different environments, we considered the use of convolutional neural networks (CNNs) to detect bridge cracks. CNNs was first proposed byLeCun et al. [8]. It was widely used in image classification [9; 10; 11; 12], object detection [13], and action recognition [14] due to the outstanding learning capability. The deeper network in CNNs can further extract the feature of feature map, which is obtained by shallow network. Influenced by this characteristic of CNNs, researchers have been devoted to the field of pixel-level crack detection. CrackNet [15] was applied to crack detection in 2017. Unlike the traditional CNN structure, it removed pooling layers to avoid loss of detail due to over downsampling. Though high detection accuracy can be achieved, the feature generator used in CrackNet produced handcrafted features using predesigned line filters, which leads to the limitations in learning capability. CrackNet-V [16] was modified on the basis of CrackNet, and it had a deeper structure and fewer parameters. At the same time, CrackNet-V improved detection efficiency and reduced calculation cast. CrackSeg [17] and U-Net [18] can also get high detection accuracy. However, like other pixel-level crack detection algorithms, they need to label each pixel of each image in the datasets, so the production of the datasets is a complex project. Meanwhile, the large number of parameters and long training time of pixel-level crack detection make it less feasible in practical engineering. In order to meet the fast and accurate requirements in crack detection task, researchers have designed classifiers to judge whether the image cells are cracks. Artificial Neural Networks (ANNs) [19; 20; 21] and Support Vector Machines (SVMs) [22; 23] have been verified to perform classifications on crack detection. However, these techniques generally represent only a few layers of abstraction and could not fully understand the complexity of bridge surface [16]. [PERSON] et al. [24] proposed a network based on CNNs and combined with the sliding window technique, which can detect images with a resolution greater than 256 \(\times\) 256 pixels. [PERSON] et al. [25] designed an end-to-end model based on the Atrous Spatial Pyramid Pooling (ASPP) module, achieving a detection accuracy of 96.37%. Although these methods could complete the task of crack detection, the accuracy and computational complexity of the detection can be further improved.