Work

Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection

Year: 2025

Type: article

Abstract: Road damage detection involves identifying cracks, potholes, and other surface irregularities from collected images. This technology is crucial for road maintenance and ensuring traffic safety. Despite significant progress in object detection algorithms, challenges such as weather-induced variability, dispersed key features, and diverse forms of damage persist. To address these issues, this paper proposes a road damage detection algorithm named Flexi-Weather Hard Detection, which integrates data augmentation based on AIGC and corner point feature aggregation. One of the modules, named Weather Trim Augment, utilizes stable diffusion technology to generate road damage data under various weather conditions. This enhancement expands the training dataset and reduces the negative impact of weather on detection accuracy. The Flexi Corner Block Block, utilizes deformable convolutions and combines a lightweight MLP with a learnable visual center mechanism to leverage corner points, enhancing local feature learning and improving the detection of subtle and dispersed features in a multi-scale context. Additionally, the HXIOU loss function is designed, employing weighted calculations and multiple metrics to effectively mine hard examples with significant variability, thus enhancing the detection accuracy of difficult cases such as blurred potholes and fine cracks. Comprehensive experiments on the RDD2020 and CNRDD datasets demonstrate that the proposed approach significantly improves performance, achieving 64.9% in the Test1 metric and 40.6% in the F1-Score. Notably, the algorithm achieves robust detection in unannotated, adverse weather conditions such as snow and rain, showcasing excellent eneralization capabilities.

Source: IEEE Access

DOI: 10.1109/access.2025.3561831

Authors: Zhaohui Wu, Zhaojia Li, XingLiang Sun, Runjing Zhao, Yingduo Bai, Jiang Na

Institutions: China Academy of Transportation Sciences, Capital Normal University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,075