DCDRNet: Detail–Context Decoupled Representation Learning Network for Efficient Crack Segmentation

Accurate crack segmentation is critical for automated infrastructure inspection but remains challenging due to the inherent conflict between preserving fine-grained geometric details and modeling global semantic context. Existing deep learning approaches typically encode both requirements within a single hierarchical representation, leading to irreversible boundary degradation or fragmented predictions under complex backgrounds. To address this limitation, we propose DCDRNet, a detail–context decoupled network that explicitly separates geometry-sensitive and context-aware representations into parallel encoding streams. The Detail Encoder maintains high-resolution features to preserve thin crack boundaries, while the Context Encoder performs adaptive global reasoning to reinforce structural continuity. Their controlled interaction enables effective integration of local precision and long-range context without representational interference. Extensive experiments on three public crack segmentation benchmarks demonstrate that DCDRNet consistently outperforms state-of-the-art methods in accuracy and robustness, achieving superior performance especially on challenging datasets with thin and fragmented cracks. Moreover, DCDRNet delivers a favorable accuracy–efficiency trade-off, combining compact model size with near real-time inference speed, making it well-suited for practical deployment in real-world inspection scenarios.