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A platform for research: civil engineering, architecture and urbanism
Network for robust and high-accuracy pavement crack segmentation
Abstract Timely segmentation and repair of pavement cracks significantly impacts both traffic safety and the service life of the roadway. However, current crack segmentation algorithms are limited in terms of imprecise segmentation and poor robustness. To overcome current limitations, this study proposes a pavement crack segmentation algorithm called MixCrackNet. MixCrackNet leverages deformable convolution, weighted loss functions, an efficient multi-scale attention module, and the Mix Structure to identify pavement cracks. Three datasets were used to train and validate the effectiveness of MixCrackNet. By comparing with classical semantic segmentation networks, the results demonstrate that MixCrackNet outperforms all the other models in crack segmentation. Furthermore, MixCrackNet not only exhibits exceptional performance across all three datasets, but also achieves decent results in untrained dataset. These results indicate that MixCrackNet is not only highly accurate but also robust, thereby promoting the application of semantic crack segmentation technology in pavement condition detection.
Highlights We propose a highly accurate and robust model for pavement crack segmentation. The EMA attention mechanism is used to encode global information and aggregate parallel output feature maps. The Mix Structure is proposed to fuse shallow and deep features of the ResNet50 network. We propose an upsampling module based on deformable convolution that can greatly improve crack segmentation accuracy.
Network for robust and high-accuracy pavement crack segmentation
Abstract Timely segmentation and repair of pavement cracks significantly impacts both traffic safety and the service life of the roadway. However, current crack segmentation algorithms are limited in terms of imprecise segmentation and poor robustness. To overcome current limitations, this study proposes a pavement crack segmentation algorithm called MixCrackNet. MixCrackNet leverages deformable convolution, weighted loss functions, an efficient multi-scale attention module, and the Mix Structure to identify pavement cracks. Three datasets were used to train and validate the effectiveness of MixCrackNet. By comparing with classical semantic segmentation networks, the results demonstrate that MixCrackNet outperforms all the other models in crack segmentation. Furthermore, MixCrackNet not only exhibits exceptional performance across all three datasets, but also achieves decent results in untrained dataset. These results indicate that MixCrackNet is not only highly accurate but also robust, thereby promoting the application of semantic crack segmentation technology in pavement condition detection.
Highlights We propose a highly accurate and robust model for pavement crack segmentation. The EMA attention mechanism is used to encode global information and aggregate parallel output feature maps. The Mix Structure is proposed to fuse shallow and deep features of the ResNet50 network. We propose an upsampling module based on deformable convolution that can greatly improve crack segmentation accuracy.
Network for robust and high-accuracy pavement crack segmentation
Zhang, Yingchao (author) / Liu, Cheng (author)
2024-03-06
Article (Journal)
Electronic Resource
English
Network for robust and high-accuracy pavement crack segmentation
| Elsevier | 2024
A lightweight feature attention fusion network for pavement crack segmentation
| Wiley | 2024