基于U型全卷积神经网络的路面裂缝检测

Pavement crack detection based on the U-shaped fully convolutional neural network

路面裂缝检测是道路运营和维护的一项重要工作,由于裂缝没有固定形状而且纹理特征受光照影响大,基于图像的精确裂缝检测是一项巨大的挑战。本文针对裂缝图像的特点,提出了一种U型结构的卷积神经网络UCrackNet。首先在跳跃连接中加入Dropout层来提高网络的泛化能力;其次,针对上采样中容易产生边缘轮廓失真的问题,采用池化索引对图像边界特征进行高保真恢复;最后,为了更好地提取局部细节和全局上下文信息,采用不同扩张系数的空洞卷积密集连接来实现感受野的均衡,同时嵌入多层输出融合来进一步提升模型的检测精度。在公开的道路裂缝数据集CrackTree206和AIMCrack上测试表明,该算法能有效地检测出路面裂缝,并且具有一定的鲁棒性。

Crack detection is one of the most important works in the system of pavement management.Cracks do not have a certain shape and the appearance of cracks usually changes drastically in different lighting conditions,making it hard to be detected by the algorithm with imagery analytics.To address these issues,we propose an effective U-shaped fully convolutional neural network called UCrackNet.First,a dropout layer is added into the skip connection to achieve better generalization.Second,pooling indices is used to reduce the shift and distortion during the up-sampling process.Third,four atrous convolutions with different dilation rates are densely connected in the bridge block,so that the receptive field of the network could cover each pixel of the whole image.In addition,multi-level fusion is introduced in the output stage to achieve better performance.Evaluations on the two public Crack-Tree206 and AIMCrack datasets demonstrate that the proposed method achieves high accuracy results and good generalization ability.