Description

In order to improve the accuracy and robustness of existing automated crack detection methods, a fully convolutional neural network for pixel-level detection based on densely connected and deeply supervised network is proposed. First, the densely connected layers are applied for enhancing the propagation and reuse of crack features. Then, the deeply supervised modules are designed to make network extract more significant features through multi-scale levels. Finally, the feature maps from different scales are fused to achieve complementarity at different levels. In addition, a class-balanced cross-entropy loss function is designed to balance backgrounds and cracks by increasing the weight of crack pixel loss. The proposed method is tested on three public datasets, and the experiments show that our method is superior to state-ofthe-art methods in accuracy, speed and robustness