# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 1 Introduction  
Under the influence of long-term cyclic loading, environmental temperature variations, and other complex external factors, pavement inevitably undergoes performance degradation and develops various types of distress, such as cracking and potholes. These distresses not only compromise the smooth operation of vehicles and reduce pavement service life but also increase rolling resistance and energy consumption, indirectly contributing to irregular carbon emissions, thereby posing challenges to environmental sustainability [1, 2, 3, 4, 5]. Moreover, pavement degradation can affect friction, which in turn compromises traffic safety, while also generating increased road noise that contributes to noise pollution in surrounding communities [6]. Furthermore, traditional pavement inspection methods rely on-frequent on-site assessments, which are energy-intensive and exacerbate the environmental burden. As a result, developing efficient and sustainable solutions for pavement distress detection has become a critical issue in the maintenance of transportation infrastructure. In the field of pavement disease detection, visual inspection has become the focus of research and application due to its simplicity, financial unpretentiousness, efficiency and intuitive nature. Traditional pavement disease visual detection methods mostly relies on manually designed criteria and algorithmic frameworks, such as using presupposed geometry templates or gray-scale thresholds to detect pavement disease [7]. Though such approaches can detect pavement diseases to a certain extent, the accuracy and generalization are relatively limited. Then, the application of techniques like edge detection indeed improves the detection performance [8], but these methods still depend on specific prior information and feature engineering. Along with the rapid development of convolutional neural networks (CNN), deep learning (DL) methods have shown remarkable success in a range of computer tasks including image classification, object detection and image segmentation [9], and have brought revolutionary changes to the field of pavement disease detection. By automatically learning complex visual features of pavement diseases, DL based models are able to more accurately identify various types of pavement diseases, demonstrating stronger generalization ability and operational efficiency. More importantly, DL driven visual methods have significant advantages in terms of detection cost [10], that is, they usually do not require complex sensor equipment and can achieve disease detection only through images captured by ordinary cameras or even smartphones, thus greatly simplifying the deployment and maintenance of detection systems. According to whether region proposals are required, DL based pavement disease visual detection can be basically divided into two-stage and one-stage methods. Two-stage approaches firstly generate region proposals and then refine them to obtain final detection results. Typically, R-CNN [11], Fast R-CNN [12] and Faster R-CNN [13] have been widely used in pavement disease detection. For example, [PERSON] et al. [14] compared R-CNN and Faster R-CNN for road damage detection. [PERSON] et al. [15] focused on the detection of longitudinal cracks, transverse cracks, alligator cracks, and partial patching in pavement images and proposed a framework for applying the Faster R-CNN technique to full-size pavement images. Based on Faster R-CNN, [PERSON] et al. [16] realized precise and stable recognition and location of pavement distress in different light conditions. Similarly, a pre-trained Faster R-CNN model was adopted to detect cracks, and a parametric study was carried out by changing the shooting conditions on the concrete road [17]. [PERSON] et al. [18] achieved automatic identification of road cracks by constructing a Mask R-CNN model. As a whole, most of two-stage methods suffer from high computation cost and unsatisfactory detection speed, which partly affect their applications in real-world pavement disease detection.