Abstract: Urban underground pipeline defect detection is essential for ensuring the normal operation of underground pipeline systems. Due to the diversity, complex shapes, and varying scales of underground pipeline defects, existing detection methods often suffer from insufficient accuracy, resulting in many false positives and missed detections. This paper proposes an effective underground pipeline defect detection model, MEG-DETR, based on the RT-DETR framework.A Multi-scale Attention-based Intra-scale Feature Interaction (M-AIFI) module is designed, which combines Multi-scale Multi-head Self-attention(M2SA) to establish channel and spatial dependencies within high-level semantic features, enabling the comprehensive capture of fine-grained defect features. A Spatial Prior Multi-scale Feature Pyramid Network(SP-MSFPN) is constructed, introducing Efficient Local Attention (ELA) and adding a shallow feature layer to achieve efficient fusion across different scales, enhancing detection of small defects. Furthermore, a Gated Semantic Enhancement Module(GSEM) is developed, combining a multi-scale convolutional gated linear unit and a GSBottleneck to achieve collaborative enhancement of semantic and structural features, improving representation of complex defect semantics and structural details. Experimental results show that MEG-DETR achieves higher accuracy in underground pipeline defect detection, with an mAP of 83.44%, an improvement of 2.74% over the baseline; Precision and Recall increase by 1.69% and 3.03%, respectively. Compared with mainstream detection models, MEG-DETR demonstrates superior overall performance, verifying its effectiveness in complex defect scenarios.

摘要： 城市地下管道缺陷检测是保障地下管道系统正常运行的重要措施。由于城市地下管道缺陷种类多样、形态复杂及尺度多变，导致现有检测方法检测精度不足，存在较多误检和漏检问题。基于RT-DETR模型提出一种有效的地下管道缺陷检测模型MEG-DETR。设计基于多尺度注意力的尺度内特征交互模块（M-AIFI），通过结合多尺度多头自注意力（M2SA），在高语义层特征内部建立通道与空间依赖关系，实现对地下管道缺陷细微特征的充分捕获；构建空间先验多尺度特征金字塔网络（SP-MSFPN），引入高效局部注意力机制（ELA）及添加浅层特征层，实现不同尺度层级间特征的高效融合，增强对小目标缺陷的检测能力；设计门控语义增强模块（GSEM），结合多尺度通道门控线性单元和GSBottleneck模块，实现语义与结构特征的协同增强，提升模型对复杂缺陷语义和细节结构的表征能力。实验结果表明，所提出的MEG-DETR模型能够更准确地检测地下管道缺陷，mAP达到83.44%，较原始模型提升2.74%；Precision和Recall分别提升1.69%和3.03%。与主流检测模型进行对比，MEG-DETR整体性能均优于其他主流方法，验证了模型在复杂缺陷场景下的有效性。