YOLO-R:road damage detector based on dynamic receptive fields and lightweight design

To address the challenges of missed detection, false detection, and low efficiency in road damage detection, this paper proposes an enhanced road damage detection model YOLO-R based on improved YOLOv8. First, we construct a collaborative module combining Switchable Atrous Convolution (SAC) with C2f....

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Vydáno v:Signal, image and video processing Ročník 19; číslo 11; s. 885
Hlavní autoři: Mao, Jinghong, Wang, Mei, Liu, Zhenghong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Springer Nature B.V 01.11.2025
Témata:
Accuracy
Algorithms
Boxes
Complexity
Computing costs
Convolution
Cracks
Damage detection
Deep learning
Efficiency
Feature extraction
Modules
Object recognition
Real time
Roads & highways
China
ISSN:1863-1703, 1863-1711
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Shrnutí:To address the challenges of missed detection, false detection, and low efficiency in road damage detection, this paper proposes an enhanced road damage detection model YOLO-R based on improved YOLOv8. First, we construct a collaborative module combining Switchable Atrous Convolution (SAC) with C2f. This module achieves precise multi-scale global feature extraction through dynamic adjustment of receptive fields. Second, an Adaptive Downsampling module (ADown) is designed to reduce computational complexity while maintaining feature representation capability. Furthermore, a Slim-neck module based on lightweight Group Shuffle Convolution (GSConv) and GSbottleneck is developed using VoV-GSCSP architecture to significantly improve feature fusion efficiency. Finally, the Shape-IoU bounding box regression loss function is introduced to improve localization accuracy through geometric prior optimization. Experiments on the cross-regional CRDDC2022 dataset (covering China and the US) demonstrate that YOLO-R achieves 76.3% mAP@0.50 while maintaining high inference speed, outperforming the baseline model by 3.7%. The model parameters and computational costs were reduced by approximately 12% and 33%, respectively. Comparative experiments confirm the model’s effectiveness and superiority in real-time road damage detection in complex scenarios.
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ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-025-04523-8