Dynamic multi-scale fusion network for road damage detection in complex street views

With the escalating deterioration of road surfaces, caused by surging traffic volumes and changing climatic conditions, road damage detection (RDD) has emerged as a critical task for ensuring traffic safety and prolonging pavement service life. However, in complex street scenes, the existing RDD methods still present numerous challenges: 1) The damage dimensions may vary drastically from slender cracks to large-area potholes, which leads to difficulties in multi-scale feature extraction; 2) narrow damages, such as cracks, usually exhibit curved and irregular geometric characteristics, which will complicate accurate localization. To tackle the above challenges, we propose a new RDD method based on YOLOv11 to improve the feature extraction capability in the backbone and neck, called dynamic multi-scale fusion network (DMSFNet). To make the detection of road damage more accurate on vast scale variation and slender morphological patterns, DMSFNet introduces a group fusion block (GFB) and a multi-branch feature fusion (MBFF) module. The GFB with multi-scale hybrid convolution architecture integrates grouping and aggregation mechanisms to explicitly model discrete scale-specific features, thereby significantly enhancing accuracy for damage of all sizes. MBFF employs three branches for the contextual features of the surrounding environment and geometric features of damage by dynamic snake convolution. Additionally, a lightweight efficient channel attention (ECA) is used for fusing the multi-branch features to enhance representation capability. Extensive experiments on the SVRDD, RDD2020, and USRDD datasets demonstrate the effectiveness of our method, which achieves a favorable trade-off between detection accuracy and computational efficiency. The employed datasets can be obtained by RDD2020 ( https://data.mendeley.com/datasets/5ty2wb6gvg/1 ), SVRDD ( https://zenodo.org/records/10100129 ) and USRDD ( https://doi.org/10.6084/m9.figshare.21431547 ).