Pavement disease object detection for UAVs based on improved YOLOv8

Jinbo Guo; Fenghua Xu; Shenghuai Wang; Xiaohui Chen; Chen Wang; Wei Zhang

doi:10.1117/12.3037809

12 September 2024 Pavement disease object detection for UAVs based on improved YOLOv8

Jinbo Guo, Fenghua Xu, Shenghuai Wang, Xiaohui Chen, Chen Wang, Wei Zhang

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132560K (2024) https://doi.org/10.1117/12.3037809
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinbo Guo, Fenghua Xu, Shenghuai Wang, Xiaohui Chen, Chen Wang, and Wei Zhang "Pavement disease object detection for UAVs based on improved YOLOv8", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132560K (12 September 2024); https://doi.org/10.1117/12.3037809

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KEYWORDS

Object detection

Data modeling

Unmanned aerial vehicles

RGB color model

Detection and tracking algorithms

Head

Education and training

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