A Fast Automatic Road Crack Segmentation Method Based on Deep Learning with Model Compression Framework

Abstract

Computer-vision and deep-learning techniques are widely applied to detect, monitor, and assess pavement conditions including road crack detection. Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection. Complex network model can generate redundant feature maps and computational complexity. Therefore, this paper proposes a novel model compression framework based on deep learning to detect road cracks, which can improve the detection efficiency and accuracy. A distillation loss function is proposed to compress the teacher model, followed by channel pruning. Meanwhile, a multi-dilation model is proposed to improve the accuracy of the model pruned. The proposed method is tested on the public database CrackForest dataset (CFD). The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.

Keywords

automatic road crack detection / deep learning / U-net / distillation / channel pruning / multi-dilation model