# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 2 Related Work  
### Road Damage Detection  
Conventional road damage detection approaches are mainly based on vehicle patrols, manual photography, and inspection personnel. This is laborious, inefficient, and often needs lots of manpower and resources. Additionally, during inspections, patrol vehicles and personnel often need to occupy the road, leading to potential traffic congestion and safety hazards. Moreover, manual detection heavily relies on the experience of inspection personnel and is subject to a certain degree of subjectivity. With the ongoing development of computer vision, image segmentation techniques based on manual feature extraction have emerged. These techniques can identify road damage by extracting characteristics such as edge information [4], histograms of oriented gradients [5], and wavelet transforms [6]. However, these methods often require manual design of feature extraction algorithms, have limited ability to capture semantic information, cannot fully utilize the rich features present in road damage images, exhibit poor generalization, and have limited effectiveness in complex scenarios.  
Deep learning-based object detection algorithms offer a new solution to road damage detection. These methods can automatically identify the precise location, type, and boundary information of damages in images, enabling rapid and precise detection of road damage. Currently, many deep learning-based object detection algorithms have been utilized for road detection tasks. [PERSON] et al. [7] proposed a convolutional neural network (CNN)-based supervised road surface crack detection technique, showing excellent performance in handling different road textures. [PERSON] et al. [8] tested eight different road damage detection models based on Single Shot MultiBox Detector (SSD) and Faster Region-CNN(R-CNN), providing a performance comparison. [PERSON] et al. [9] created a method for detecting, localizing, and quantifying cracks by integrating Faster R-CNN, improved tubularity flow field (TuFF), and improved distance transform method (DTM) to detect crack areas, addressing practical issues with complex environmental backgrounds. [PERSON] et al. [10] deployed SOTA deep learning algorithms based on different network backbones to detect and characterize pavement distresses. They investigated the impact of different backbone architectures including CSPDarknet53, Hourglass-104, and EfficientNet on the classification performance. [PERSON] et al. [11] introduced a Mask R-CNN framework employing DenseNet as the feature map extraction backbone. They utilized a feature pyramid network to integrate features across multiple scales, a region proposal network for generating road damage regions, and a fully convolutional neural network to classify and refine region boundaries. This approach enables not only the detection and classification of road damage but also the creation of a mask of the road damage. [PERSON] et al. [12] introduced an innovative privacy-preserving edge cloud and federated learning-based framework named FedRD for intelligent detection and warning of hazardous road damage. This method provides inexpensive, quick, precise, and private road damage detection and warning. [PERSON] et al. [13] proposed a multi-level attention mechanism named Multi-level Attention Block (MLAB) to enhance the utilization of critical features by YOLOv3. The network can accurately detect longitudinal cracks, transverse cracks, repairs, and potholes and notably enhances the accuracy of detecting alligator cracks and oblique cracks. [PERSON] et al. [14] introduced an improved YOLOv5 road surface damage detection method, GSC-YOLOv5, enhancing the detection capability for small cracks and improving accuracy while reducing the model's parameter count. [PERSON] et al. [15] introduced a three-stage approach for bridge road damage and lane positioning based on YOLOv7 and improved LaneNet. This approach attained satisfactory outcomes in bridge road damage detection and lane positioning tasks.