Abstract
Early detection of fine cracks is crucial for preventing structural failures in civil engineering. Conventional manual inspection approaches for structural surface cracks often suffer from limited operational efficiency and inevitable subjective bias, as visual assessment heavily relies on inspectors’ personal experience, fatigue levels, and on-site lighting conditions. In contrast, deep-learning-based crack detection methods have become mainstream alternatives to manual inspection. However, these approaches encounter a host of practical challenges when deployed on unmanned inspection platforms. Complex background interference limits the accurate extraction of fine-grained crack features; furthermore, insufficiently annotated datasets and cumbersome network architectures that fail to support lightweight real-time inference jointly degrade model generalization in diverse field operating environments. To address these issues, this paper proposes a Lightweight Defect Detection YOLOv11 Network (LDD-YOLOv11), a crack detection algorithm that combines directional multi-scale feature enhancement with a teacher–student knowledge distillation mechanism. The technological innovations of this research are twofold. On the one hand, LDD-YOLOv11 embeds two dedicated feature enhancement modules, namely the Light-Material Aware Adapter (LMA) and the Direction-Material Constrained Focus (DMC-Focus) module, into the original YOLOv11 backbone and neck to strengthen multi-scale fine crack feature representation. On the other hand, we adopt a tailored Dual Perception Focusing Distillation (DPFD) strategy to boost feature-level knowledge transfer and improve overall generalization robustness. Evaluated on a self-constructed urban building crack dataset, the proposed model achieves a mean average precision at 0.5 (mAP@0.5) of 90.50% with only 3.20 M parameters and an inference speed of 67.7 FPS. Compared with the fully trained YOLOv11n baseline (88.30% mAP@0.5), mAP@0.5 is increased by 2.2 percentage points while maintaining favorable lightweight characteristics. Furthermore, its accuracy and efficiency surpass those of mainstream crack detection models such as U-Net and DeepCrack. Cross-dataset generalization experiments and edge deployment simulation further validate the model’s adaptability to complex scenarios and practical deployment potential. Consequently, LDD-YOLOv11 proves effective for automatic structural crack inspection and demonstrates significant engineering application value.