基于DenseNet分类的隧道裂缝检测研究

针对隧道裂缝人工识别低效、检修不便以及隧道环境复杂多变、检测易受噪声干扰等问题，文中提出一种基于深度学习的裂缝检测算法。通过神经网络对原始图像进行非裂缝区域过滤，减少无关背景信息的干扰，同时在分割算法基础上通过多维分类器将误识别的裂缝区域剔除。实验结果表明，密集连接卷积网络（DenseNet）在裂缝分类中最高可达99.95%的准确率，有效提升了隧道裂缝自动检测精度。

Study on Tunnel Crack Detection Based on DenseNet Classification

Aiming at the problems of inefficient manual detection of tunnel cracks, inconvenient maintenance, complicated and changeable tunnel environment, and susceptibility to noise interference, a crack detection algorithm based on deep learning is proposed. Non-crack areas are filtered through the neural network to reduce the interference of irrelevant background information. At the same time, based on the segmentation algorithm, mis-recognized crack areas are eliminated. Experimental results show that the DenseNet network can reach a maximum accuracy of 99.95% in crack classification, which effectively improves the accuracy of tunnel crack detection.