Dynamic receptive field and feature enhanced network for bridge defect detection

Introduction Accurate defect detection in underwater and cross-domain marine infrastructure, such as bridge piers, submarine pipelines, and subsea equipment, is essential for structural safety assessment and intelligent ocean engineering. Existing methods, however, suffer from large scale variations, dense small targets, severe class imbalance, and texture loss in complex underwater environments. Methods We propose a multi–dimensional collaborative detection network based on YOLOv10n. In the backbone, switchable dilated convolutions (SAConv) adaptively adjust receptive fields, and a spatial-channel downsampling (SCDown) module preserves fine-grained details. At the backbone output, a Partial Spatial Attention and Multi-layer Latent Linear Attention (PSAMLLA) module captures global context with linear complexity via rotary and local position encoding. In the feature pyramid, a Conditional Identity Block Feature Fusion (C2fCIB) module enhances multi-scale fusion using deep large-kernel convolutions with structural re-parameterization. Finally, a Dynamic Detection Head (DyHead) integrates scale-aware, spatial-aware, and task-aware attention for adaptive inference. Results Extensive experiments on the public bridge-bkqxz/2 dataset demonstrate that our method achieves comparable or better results than several state-of-the-art detectors while maintaining low computational cost. Discussion Although the current experiments are limited to bridge surface defect detection, the proposed techniques address challenges such as texture degradation and multi-scale object variability and may provide a useful foundation for future extension to underwater vision systems, which remain an important direction for subsequent investigation.