To cope with the complex bridge crack detection environment, we developed a flexible crack identification system. Firstly, the acquired images are processed by sliding window technology to construct a bridge crack dataset. Then we propose a trainable context encoder network which uses the recurrent residual convolutional neural network (RRCNN) to improve the encoder structure to better extract low-level features. Additionally, it combines dense atrous convolution block (DAC) and residual multi-kernel pooling block (RMP) which can retain more crack information and features from the crack image, as well as improve the performance of crack segmentation. Finally, our research results are validated in the developed software platform. The experiments show that our method is more stable and accurate than other methods, its accuracy and mean intersection over union (mIoU) have reached 98.62% and 80.93%, respectively. Moreover, it performs well in cracks facing complex conditions, which can be applied to the bridge maintenance projects.

为了应对复杂的桥梁裂缝检测环境，我们开发了一种灵活的裂缝识别系统。首先，利用滑动窗口技术对采集到的图像进行处理，以建立桥梁裂缝数据集。然后，我们提出了一种可训练的上下文编码器网络，该网络使用递归残差卷积神经网络（RRCNN）来改进编码器结构，以更好地提取低级特征。此外，它结合了密集的多孔卷积块（DAC）和残余多核池化块（RMP），可以保留更多裂纹信息和裂纹图像特征，并提高了裂纹分割的性能。最后，我们的研究结果在开发的软件平台中得到了验证。实验表明，我们的方法比其他方法更稳定，更准确，它的准确性和均值交会（mIoU）分别达到98.62％和80.93％。而且，它在面对复杂条件的裂缝中表现良好，可用于桥梁维修项目。