# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 1 Introduction
To overcome the above-mentioned problems, researchers have made a series attempts to achieve one-stage objection, and the single shot multi-box detector (SSD) [19] and you only look once (YOLO) [20] are the representatives, which have shown significant advantages in the field of pavement disease detection by virtue of highly efficient detection speeds and low computational costs. For instance, the improved SSD has been found to be effective in recognizing rural road damages, offering faster recognition speed and higher recognition accuracy [21]. Compared with the R-CNN based methods, one-stage methods do not need to generate candidate regions, but directly generate dense predictions on images, which can not only greatly accelerate the process of detecting pavement diseases such as cracks and potholes, but also significantly reduce the demand for computational resources, making them more suitable for real-time application scenarios [22]. With the continuous optimization and improvement, one-stage methods especially YOLO series have been widely used in practical pavement disease detection tasks. The tiny version of YOLOv3 has been applied to road crack detection, where the feature extraction process was optimized by adding three new convolutional layers, which proved effective in improving detection accuracy [23]. Multiple YOLO models have been explored for detecting and classifying various types of pavement diseases, providing strong technical support forward maintenance and management [24]. YOLO networks have also been used to predict the possible locations and categories of pavement distresses, achieving a comprehensive detection accuracy of 73.64%, with a discussion of their applicability under different illumination conditions [25]. By introducing the MobileNet [26], YOLO model size was significantly reduced, and detection accuracy was improved through the coordinate attention (CA) [27] and the clustering algorithm [28]. To address issues in road damage detection, such as single detection type, low efficiency and difficulty in detecting targets, the depth and width of YOLOv5s were optimized, with the introduction of ghost and space-to-depth convolution modules, which demonstrated significant advantages in detection and classification [29]. An intelligent model for pavement crack detection was proposed, improving YOLOv5 by enhancing the detection layer, anchor box, neck structure, and cross-layer connection, which boosted feature extraction and small target detection performance [30]. Another study integrated UAVs with binocular cameras, combining YOLOv5s with an efficient channel attention mechanism to enhance detection accuracy, resulting in a 5.30% improvement in mAP and better crack quantification compared to the baseline [31]. Additionally, a real-time road pothole detection method based on YOLOv8 was proposed, utilizing techniques such as contrast stretching adaptive Gaussian star filter and edge segmentation to improve detection performance [32]. A recent improvement to YOLOv8 for road defect detection introduced SPD-Conv layers, an ASF-YOLO neck, and FasterNet blocks, achieving a 2.8% improvement in mAP and a detection speed of 43 FPS, making it highly efficient for real-time road maintenance [33]. An enhanced YOLOv8 network, incorporating squeeze and extraction attention modules and the Swin Transformer module, was developed to accurately distinguish different types of pavement distress and suppress complex background interference [34]. Although YOLOv8 based models have gained widespread attention for their efficient detection speed and high accuracy in pavement disease detection, they still face some limitations when confronted with more complex and challenging scenarios.