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A Novel Detection Method for Pavement Crack with Encoder-Decoder Architecture

Computer Modeling in Engineering & Sciences 2023年第10期137卷 761-773页

作者： Yalong Yang Wenjing Xu Yinfeng Zhu Liangliang Su Gongquan Zhang Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving Anhui Jianzhu UniversityHefei230022China Anhui Institute of Strategic Study on Carbon Dioxide Emissions Peak and Carbon Neutrality in Urban-Rural Development Hefei230022China School of Electronic and Information Engineering Anhui Jianzhu UniversityHefei230601China School of Mechanical and Electrical Engineering Anhui Jianzhu UniversityHefei230601China

As a current popular method,intelligent detection of cracks is of great significance to road safety,so deep learning has gradually attracted attention in the field of crack image *** nonlinear structure,low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep ***,an end-to-end deep convolutional neural network(AttentionCrack)is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack *** AttentionCrack network is built on U-Net based encoder-decoder architecture,and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance the recognition of crack ***,a dilated convolution module is introduced in the encoder-decoder architecture to reduce the loss of crack detail due to the pooling operation in the encoder ***,since up-sampling will lead to the loss of crack boundary information in the decoder network,a depthwise separable residual module is proposed to capture the boundary information of pavement *** AttentionCrack net on public pavement crack image datasets named CrackSegNet and Crack500 is trained and tested,the results demonstrate that the AttentionCrack achieves F1 score over 0.70 on the CrackSegNet and 0.71 on the Crack500 in average and outperforms the current state-of-the-art methods.

关键词： Crack detection deep learning convolution neural network pixel segmentation

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