In tunnel construction engineering, the form of tunnel void diseases are complex and easily affected by the geographical environment. The traditional manual interpretation of image data has the characteristics of heavy workload, high probability of missing, and misjudgment. This paper constructs a convolution neural network that integrates the mechanism of guiding anchoring to detect tunnel voids. The network is composed of four parts: Feature extraction network extracts disease features from the enriched samples; Region proposal by guided anchoring join the generalized intersection over union (GIoU) evaluation criteria, and predict the shape of the anchor point through learning; The obtained feature maps are fixed in the region of interest pooling; Finally, the disease features are classified and bounding box regression. Compared with the existing target detection algorithm, the experimental results show that the improved network achieves an average classification accuracy of 92.74%, and the trained model has good generalization ability and robustness.

在隧道建设工程中，隧道空洞病害形式复杂，易受地理环境影响。传统的人工解译影像数据具有工作量大、遗漏概率高、误判等特点。本文构建了一个卷积神经网络，它集成了引导锚定的机制来检测隧道空隙。该网络由四部分组成：特征提取网络从丰富的样本中提取疾病特征；通过引导锚定的区域提议加入广义联合交叉（GIoU）评估标准，并通过学习预测锚点的形状；得到的特征图固定在兴趣池中；最后，对疾病特征进行分类和边界框回归。