An RPP-YOLOv11 model for road crack detection

Yuhong Xue, Ligang Zheng, Yangyang Shi and Jiafeng Bai

PLOS ONE, 2026, vol. 21, issue 8, 1-23

Abstract: Accurate and efficient road crack detection serves as a critical component in smart transportation systems and infrastructure maintenance. Existing YOLO series models still exhibit limitations in detecting cracks due to their sensitivity to subtle details, diverse morphological variations, and complex background interference from road surfaces. Building upon YOLOv11, this study enhances road crack detection capabilities through three core innovations: 1) Implementing an early-stage fusion strategy combining visible light and thermal infrared images (RGBT) to improve environmental adaptability; 2) Adopting windmill-shaped convolution modules (PSConv) to replace traditional convolutions, thereby enhancing crack feature extraction while suppressing background noise; 3) Introducing a P6 detection layer to establish a four-scale detection framework (P3-P6), expanding global perception capabilities for large-scale cracks. Experiments on the cross-border road damage dataset RDD2022 demonstrate that the proposed RPP-YOLOv11 model (integrating RGBT multispectral fusion, PSConv convolution modules, and P6 detection layer) achieves 74.90% accuracy, 64.36% recall rate, and 69.04% mAP@0.5 with 42.70% mAP@0.5:0.95. Compared to original YOLOv11 and mainstream benchmarks, this model shows significant improvements in detection precision, robustness, and computational efficiency, providing a reliable technical solution for automated road inspection systems.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355111 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 55111&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0355111

DOI: 10.1371/journal.pone.0355111

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().