为解决地铁隧道衬砌裂缝智能识别中，由于隧道环境复杂、裂缝形态多样，通过目标识别算法难以给出裂缝形态及其物理信息，以及传统语义分割算法存在掩码断裂，分割误差大、效率低等问题，提出改进YOLOv11隧道衬砌裂缝分割模型SGE-YOLO(spatial group-wise enhance-yolo)。模型旨在克服现有方法的局限性，在确保高识别精度的前提下，提升检测效率。SGE-YOLO模型结构采用空间分组增强注意力机制优化，分别实现浅、中、深3层多尺度特征的融合增强，突出裂缝识别时的关键特征，减少干扰特征。同时，提出基于YOLOv11的隧道衬砌裂缝的改进滑动推理方法，以提升裂缝识别效率。为有效解决裂缝分割掩码断裂问题，通过融合单连通域识别、形态学闭运算、骨架提取方法，提出邻近端点对匹配及距离角度阈值筛选的裂缝形态学优化算法，以确保裂缝掩码的连续性。与多种分割模型对比试验表明： 1) SGE-YOLO模型相较于常规YOLOv11模型，分割裂缝效率提升324%，误判率降低75%； 2) SGE-YOLO参数量，相较原始YOLOv11模型仅增加了0.2 MB，单论训练时长仅增加8.4 s，可支持工程中的计算与时间效率要求； 3) 使用滑动推理及形态学修正算法后，SGE-YOLO误判率显著降低，掩码断裂情况改善，识别分割的速度提升约434%，证明了滑动推理及形态学修正算法对于裂缝分割存在准确率与误判率的积极作用。
In intelligent identification of lining cracks of metro tunnels, it is difficult to provide physical information of the crack morphology through target recognition algorithms due to complex tunnel environment and diverse crack shapes. Moreover, traditional semantic segmentation algorithms suffer from mask fragmentation, large segmentation errors, and low efficiency. To address these challenges, an improved YOLOv11 tunnel lining crack segmentation model—SGE-YOLO (Spatial Group-wise Enhance-YOLO)—is proposed. The model aims to overcome the limitations of existing methods and improve detection efficiency while ensuring high recognition accuracy. The SGE-YOLO model structure adopts an optimized spatial group-wise attention mechanism to achieve the fusion and enhancement of multi-scale features at the shallow, medium, and deep layers, highlighting the key features for crack recognition and reducing interfering features. Additionally, an improved sliding inference method for tunnel lining cracks based on YOLOv11 is proposed to enhance the efficiency of crack recognition. To effectively solve the problem of mask fragmentation in crack segmentation, a crack morphology optimization algorithm based on single connected domain recognition, morphological closing operation, and skeleton extraction methods is proposed to ensure the continuity of the crack mask. Comparative experiments with various segmentation models yield the following results: (1) The SGE-YOLO model exhibits a 324% increase in crack segmentation efficiency compared to conventional YOLOv11 model, and a 75% reduction in misjudgment rate. (2) The parameters of the SGE-YOLO model only increase by 0.2 MB compared to the original YOLOv11 model, and the training time only increases by 8.4 s, which can meet the computational and time efficiency requirements in engineering. (3) After using the sliding inference and morphology correction algorithms, the misjudgment rate of SGE-YOLO is significantly reduced, the mask fragmentation is improved, and the speed of recognition and segmentation is increased by approximately 434%, proving the positive effect of the sliding inference and morphology correction algorithms on the accuracy and misjudgment rate of crack segmentation.
