A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pavement crack detection based on transformer network
Abstract Accurate pavement surface crack detection is essential for pavement assessment and maintenance. This study aims to improve pavement crack detection under noisy conditions. A novel model named Crack Transformer (CT), which unifies Swin Transformer as the encoder and the decoder with all multi-layer perception (MLP) layers, is proposed for the automatic detection of long and complicated pavement cracks. Based on a comprehensive investigation of training performance metrics and visualization results on three public datasets, the proposed CT model indicates enhanced performance. Experimental results prove the effectiveness and robustness of the Transformer-based network on accurate pavement crack detection. This study shows the feasibility of using a Transformer-based network for automatic robust pavement crack detection under noisy conditions.
Highlights A novel approach for automatic pavement crack inspection based on transformer network is proposed. The proposed CT model can model the long-range pavement crack pixels with high accuracy and efficiency. A new pavement crack image dataset named CrackSC is established and released to public.
Pavement crack detection based on transformer network
Abstract Accurate pavement surface crack detection is essential for pavement assessment and maintenance. This study aims to improve pavement crack detection under noisy conditions. A novel model named Crack Transformer (CT), which unifies Swin Transformer as the encoder and the decoder with all multi-layer perception (MLP) layers, is proposed for the automatic detection of long and complicated pavement cracks. Based on a comprehensive investigation of training performance metrics and visualization results on three public datasets, the proposed CT model indicates enhanced performance. Experimental results prove the effectiveness and robustness of the Transformer-based network on accurate pavement crack detection. This study shows the feasibility of using a Transformer-based network for automatic robust pavement crack detection under noisy conditions.
Highlights A novel approach for automatic pavement crack inspection based on transformer network is proposed. The proposed CT model can model the long-range pavement crack pixels with high accuracy and efficiency. A new pavement crack image dataset named CrackSC is established and released to public.
Pavement crack detection based on transformer network
Guo, Feng (author) / Qian, Yu (author) / Liu, Jian (author) / Yu, Huayang (author)
2022-10-29
Article (Journal)
Electronic Resource
English