# Adaptive Local–Global Synergistic Perception Network for Hydraulic Concrete Surface Defect Detection

## TL;DR

This paper introduces a new neural network for detecting defects in hydraulic concrete surfaces, which adapts to irregular shapes and suppresses background noise.

## Contribution

The paper introduces ALGSP-Net, a novel network with adaptive receptive fields and dual-stream fusion for improved defect detection in hydraulic concrete.

## Key findings

- ALGSP-Net achieves 77.46% average precision and 72.78% mAP50 on the SDD-HCS dataset.
- The method outperforms existing benchmarks in accuracy and robustness for defect detection.
- Adaptive modules effectively capture irregular defect geometries and suppress background noise.

## Abstract

Surface defects in hydraulic concrete structures exhibit extreme topological heterogeneity. and are frequently obscured by unstructured environmental noise. Conventional detection models, constrained by fixed-grid convolutions, often fail to effectively capture these irregular geometries or suppress background artifacts. To address these challenges, this study proposes the Adaptive Local–Global Synergistic Perception Network (ALGSP-Net). First, to overcome geometric constraints, the Defect-aware Receptive Field Aggregation and Adaptive Dynamic Receptive Field modules are introduced. Instead of rigid sampling, this design adaptively modulates the receptive field to align with defect morphologies, ensuring the precise encapsulation of slender cracks and interlaced spalling. Second, a dual-stream gating fusion strategy is employed to mitigate semantic ambiguity. This mechanism leverages global context to calibrate local feature responses, effectively filtering background interference while enhancing cross-scale alignment. Experimental results on the self-constructed SDD-HCS dataset demonstrate that the method achieves an average Precision of 77.46% and an mAP50 of 72.78% across six defect categories. Comparative analysis confirms that ALGSP-Net outperforms state-of-the-art benchmarks in both accuracy and robustness, providing a reliable solution for the intelligent maintenance of hydraulic infrastructure.