Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network

Abstract

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.