# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 1 Introduction  
Roods constitute the backbone of transportation and are a crucial component of urban and rural infrastructure. Well-maintained road infrastructure is a prerequisite for urbanization and economic development. Given the ongoing development in road construction in China, the scale and complexity of the road network have experienced explosive growth. As of the end of 2022, the total road mileage in China has reached 5.35 million kilometers, growing by 1.12 million km in the past decade. Among them, the mileage of highways has reached 177,000 km, ranking first globally [1]. However, as the scale of roads expands, road damage issues become increasingly prominent. Due to differences in vehicle loads and construction lifespans, these roads face varying degrees of damage. Road damages such as potholes and cracks not only affect the efficiency of road traffic but also increase the instability of vehicle travel, raising the probability of traffic accidents and posing a threat to the safety of vehicles and passengers. Therefore, to maintain road infrastructure and improve road safety, it is crucial to detect and repair these road damages in a timely manner, which places strict requirements on the task of road damage detection.  
Although the currently proposed deep learning-based object detection techniques have demonstrated remarkable success, there still remain challenges and room for enhancement in the field of road damage detection. Firstly, road damage exhibits diverse types with irregular shapes and sizes, making the detection task inherently challenging. Secondly, road background images encompass various elements such as different road surface textures, vehicles, pedestrians, and buildings, which may interfere with the accurate detection of road damage and impact the model's precision. Furthermore, the appearance of road damage varies under different lighting and weather conditions, adding complexity to the road damage detection task. To tackle these obstacles and improve the accuracy and robustness of detection, this paper improves the state-of-the-art (SOTA) YOLOv8 model and applies it to road damage detection, resulting in improved precision and performance. The main contributions of this paper are as follows:  
* The introduction of the SimAM [2] into the backbone filters out noise and amplifies the model's focus on important information within the input image. This enhancement significantly improves the model's performance and generalization ability, enhancing its ability to effectively handle road damage detection tasks.
* The neck structure is enhanced by replacing traditional convolution modules with GhostConv [3]. This not only successfully reduces redundant information in the model, lowering the number of parameters and computational complexity, but also maintains the model's outstanding performance in recognizing damage.
* To ensure the provision of more accurate images, the nearest interpolation in the neck is replaced with a more accurate bilinear interpolation. This modification enhances the model's capability to retain complex details in the image, producing a clearer, more accurate output that helps minimize the impact of environmental factors on detection results.