Pavement disease object detection for UAVs based on improved YOLOv8

Unmanned Aerial Vehicles (UAVs) pavement distress detection represents a critical task within the domain of highway maintenance. For challenges such as the broad field of view and small target size characteristic of drone-captured images, the complexity of the backgrounds, and the constraints imposed by limited-resource platforms which preclude the deployment of traditional detection models. To this end, we introduce YOLOv8-EHG, a lightweight, real-time UVAs pavement distress detection model, built upon an enhanced YOLOv8 framework. Our approach first integrates Efficient Local Attention (ELA) within a High-level Screening-feature Pyramid Networks (HSFPN) to forge the ELA-HSFPN architecture, replacing the Pyramid Attention Network (PAN) in YOLOv8. Subsequently, we developed a lightweight detection head, Detect-T3G. According to the RDD2022 dataset, this model achieves an mAP50 of 67.4%, a 0.2% improvement over the original YOLOv8. It also reduces the model parameters by 46.9% and computational complexity by 41.9%. These improvements facilitate the deployment of drones for real-time detection of road surface diseases.