Research on Road Surface Distress Detection Algorithm in UAV Images with Multi-Scale Feature Fusion.
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| Title: | Research on Road Surface Distress Detection Algorithm in UAV Images with Multi-Scale Feature Fusion. |
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| Authors: | Guo, Dudu1,2 (AUTHOR) guodd@xju.edu.cn, Cai, Wenxing2,3 (AUTHOR), Shuai, Hongbo3 (AUTHOR), Wei, Zhenxun4,5 (AUTHOR), Chen, Guoliang4,5 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1461. 21p. |
| Subjects: | Edge detection (Image processing), Wavelet transforms |
| Abstract: | Highlights: What are the main findings? An improved YOLOv8 algorithm is proposed for UAV-based road surface defect detection, incorporating four novel modules—FFDPN, IIDH, EIEM, and WaveletPool—to address insufficient feature fusion, detail loss, and small-target aliasing distortion, achieving a 12.2% increase in mAP (83.8% → 96.0%). With only 2.41 × 106 parameters, the improved model outperforms mainstream detectors, including Faster R-CNN, YOLOv9, and YOLOv11n, in Precision (93.7%), Recall (89.6%), and mAP, while effectively eliminating duplicate detections and missed detections across four defect categories. What are the implications of the main findings? The proposed lightweight, high-accuracy model provides a practical solution for automated UAV-based highway pavement inspection, supporting the digital transformation of road maintenance by reducing reliance on manual labor and lowering inspection costs and safety risks. The design principles of FFDPN and WaveletPool offer transferable methodological insights for multi-scale feature fusion and anti-aliasing downsampling in small-target detection tasks, with broad applicability to other UAV remote sensing object detection scenarios. Unmanned aerial vehicle (UAV) imagery offers a promising alternative to manual and vehicle-based inspection for highway pavement distress detection, but the high-angle perspective reduces the relative size and feature richness of small distresses and amplifies aliasing during downsampling, limiting the accuracy of existing detectors. To address these problems, this paper proposes an improved YOLOv8 algorithm with four coordinated modifications: (i) a Feature-Focusing Diffusion Pyramid Network (FFDPN) that replaces the conventional PAN to strengthen multi-scale feature fusion and preserve fine-grained details; (ii) an Information Interaction Detection Head (IIDH) that replaces the decoupled dual-branch head, sharing interaction features between the classification and regression branches via deformable convolution (DCNv2) to reduce parameters while improving task synergy; (iii) an Edge Information Extraction Module (EIEM) placed at the front of the backbone, which uses Sobel-based gradient response plus max-pooling to inject low-level edge priors; and (iv) a WaveletPool downsampling operator that decomposes features into LL/LH/HL/HH sub-bands to suppress aliasing of small-scale distresses. Experiments on 3408 UAV images of four distress categories (transverse, longitudinal, and alligator cracks and potholes) show that the improved model reaches 93.7% Precision, 89.6% Recall, and 96.0% mAP@0.50—a 12.2 percentage-point gain over YOLOv8n—while using only 2.41 × 106 parameters and outperforming Faster R-CNN, DETR, YOLOv7-tiny, YOLOv9, YOLOv10n, YOLOv11n, and YOLO-World on the same benchmark. The model eliminates the duplicate and missed detections observed in baselines, at a moderate cost in FPS (30.3 vs. 57.1 for YOLOv8n). [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An improved YOLOv8 algorithm is proposed for UAV-based road surface defect detection, incorporating four novel modules—FFDPN, IIDH, EIEM, and WaveletPool—to address insufficient feature fusion, detail loss, and small-target aliasing distortion, achieving a 12.2% increase in mAP (83.8% → 96.0%). With only 2.41 × 106 parameters, the improved model outperforms mainstream detectors, including Faster R-CNN, YOLOv9, and YOLOv11n, in Precision (93.7%), Recall (89.6%), and mAP, while effectively eliminating duplicate detections and missed detections across four defect categories. What are the implications of the main findings? The proposed lightweight, high-accuracy model provides a practical solution for automated UAV-based highway pavement inspection, supporting the digital transformation of road maintenance by reducing reliance on manual labor and lowering inspection costs and safety risks. The design principles of FFDPN and WaveletPool offer transferable methodological insights for multi-scale feature fusion and anti-aliasing downsampling in small-target detection tasks, with broad applicability to other UAV remote sensing object detection scenarios. Unmanned aerial vehicle (UAV) imagery offers a promising alternative to manual and vehicle-based inspection for highway pavement distress detection, but the high-angle perspective reduces the relative size and feature richness of small distresses and amplifies aliasing during downsampling, limiting the accuracy of existing detectors. To address these problems, this paper proposes an improved YOLOv8 algorithm with four coordinated modifications: (i) a Feature-Focusing Diffusion Pyramid Network (FFDPN) that replaces the conventional PAN to strengthen multi-scale feature fusion and preserve fine-grained details; (ii) an Information Interaction Detection Head (IIDH) that replaces the decoupled dual-branch head, sharing interaction features between the classification and regression branches via deformable convolution (DCNv2) to reduce parameters while improving task synergy; (iii) an Edge Information Extraction Module (EIEM) placed at the front of the backbone, which uses Sobel-based gradient response plus max-pooling to inject low-level edge priors; and (iv) a WaveletPool downsampling operator that decomposes features into LL/LH/HL/HH sub-bands to suppress aliasing of small-scale distresses. Experiments on 3408 UAV images of four distress categories (transverse, longitudinal, and alligator cracks and potholes) show that the improved model reaches 93.7% Precision, 89.6% Recall, and 96.0% mAP@0.50—a 12.2 percentage-point gain over YOLOv8n—while using only 2.41 × 106 parameters and outperforming Faster R-CNN, DETR, YOLOv7-tiny, YOLOv9, YOLOv10n, YOLOv11n, and YOLO-World on the same benchmark. The model eliminates the duplicate and missed detections observed in baselines, at a moderate cost in FPS (30.3 vs. 57.1 for YOLOv8n). [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101461 |
