为解决地铁隧道裂缝智能识别困难，尤其针对地铁隧道裂缝检测天窗时间短、人工检测效率低、裂缝识别特征不明显、隧道内干扰物较多等问题，提出一种基于YOLOv8的改进算法——M-YOLO（Modified-YOLO），高效智能识别隧道裂缝。M-YOLO 算法运用全维度动态卷积取代传统卷积模块，能显著提高检测的准确性，避免模型参数膨胀的问题； 引入 C2fGC 模块对网络结构进行改进，构建新的特征提取与降维机制，增强高层次特征表示； 整合CBAM 注意力机制模块，强化对裂缝区域图像的特征学习与提取，减弱背景干扰，进而有效提升检测精度； 引入 WIOU 损失函数来调节几何因素的惩罚程度，提高模型的泛化能力，在低质量数据样本下的表现更为出色。试验结果表明，在地铁隧道裂缝识别的真实样本中，M-YOLO 算法的 PmAP（平均精度均值）高达 83.0%，较原模型提高了15.7%。
Intelligently identifying
cracks in metro tunnels is challenging due to factors such as short skylight
detection time, low manual detection efficiency, inconspicuous crack
characteristics, and various disturbances in the tunnel. Therefore, a modified
YOLO (M-YOLO) algorithm based on
YOLOv8 is proposed to efficiently and intelligently identify cracks in tunnels.
This algorithm adopts full-dimensional dynamic
convolution to replace the traditional convolution module, remarkably improving
the detection accuracy and avoiding model parameter expansion. The C2fGC module
is introduced to improve the network structure, establishing a new feature
extraction and dimensionality reduction mechanism and enhancing the high-level feature representation. The convolutional block attention
module attention mechanism module is introduced to strengthen feature learning
and extraction of crack region images and reduce background interference,
effectively improving the detection accuracy. The WIOU loss function is
introduced to adjust the degree of penalty of geometric factors, which improves
the generalization ability of the model and helps it perform better even with
low-quality data samples. The test results show that in
the real samples, which are used for identifying cracks in a metro tunnel, the
mean accuracy of the M-YOLO algorithm is as high as
83.0%, which indicates a significant improvement of 15.7% compared with the
original model.
