# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 1 Introduction  
The increase in traffic volume has caused different degrees of cracks on roads, which lead to traffic accidents. At present, road crack detection usually uses manual identification, which is slow, risky, and costly. However, along with the development of image processing, the automatic processing of road crack images by using designed models could effectively solve these problems. At present, there are many algorithms for pavement crack image detection, such as the algorithm based on multi-level filtering proposed by [PERSON] [1], the crack detection algorithm based on wavelet transform proposed by [PERSON] [2], and the algorithm based on a support vector machine (SVM) proposed by [PERSON] [3]. Although these methods are more efficient than manual identification, they are sensitive to lights and shadows. With the development of deep learning, image recognition using convolutional neural networks (CNNs) has been applied in various fields. For example, [PERSON] [4] proposed a crack detection method with the R-CNN model. In his model, the locations and classifications of cracks could be detected. Although it achieved better results than the methods without deep learning, his model could not detect unobvious cracks. In addition, his model had very high computational complexities. Aiming to improve these problems, [PERSON] [5] used a one-stage detection model in target detection fields; thus, a method based on YOLO v3 [6] for pavement crack detection was designed. However, some application scenarios needed to calculate the proportion of the damaged areas, such as pavement damage area calculation. To solve the problem, some researchers proposed their methods. [PERSON] [7] enhanced crack datasets and improved the U-Net model (a symmetrical U-shaped image-segmentation network model) to design a segmentation model. In his model, every pixel of the image could be tagged as crack or non-crack classes. In addition, [PERSON] [8] proposed a model through transfer learning. In his method, general images were used to learn general features, and crack images were used for fine-tuning to learn crack features. Moreover, [PERSON] [6] used feature pyramids for pavement crack detection. In his model, the deep semantic information was integrated into the low-level convolution stage, layer-by-layer, through a new multi-scale feature fusion module. However, these methods had some evident drawbacks, which included the interferences of lights and shadows, which would lead to deviations in the prediction results since these interferences could be easily recognized as cracks by the models above. For the purpose of alleviating the interferences of lights and shadows, an attention mechanism was proposed by new researchers. For example, [PERSON] [9] proposed a combination of channel attention mechanisms and fully convolutional neural networks. [PERSON] [10] proposed a novel pavement crack detection method based on the end-to-end model and trainable deep convolutional neural networks. He built the network with an encoder-decoder architecture and used a pyramid attention module to exploit global context information for the complex topological structures of cracks. However, these models above had two evident drawbacks:  
1. These models usually used down-sampling operations to extract high-level features and recover the resolution of these features via up-sampling operations to obtain the final output. However, the down-sampling operations would reduce the resolution of features and cause feature information loss. 2. These attention-based models usually used serial structures, which is to say that they mainly focused on selecting channel features or spatial features without controlling the feature information flow. Thus, crack-related features could not be filtered by these attention-based models. In this paper, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. Thus, more low-level features could be used for the final classification.