Paper Title Tits 2024
Published:
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Real-Time Pavement Damage Detection With Damage Shape Adaptation
Published in IEEE Transactions on Intelligent Transportation Systems, 2024
In this study, we propose a fast pavement damage detection algorithm named FPDDN to achieve real-time and high-accuracy pavement damage detection.
Recommended citation: Y. Zhang and C. Liu, "Real-Time Pavement Damage Detection With Damage Shape Adaptation," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2024.3416508.
<Network for robust and high-accuracy pavement crack segmentation
Published in Automation in Construction, 2024
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.
Recommended citation: Zhang, Y., & Liu, C. (2024). Network for robust and high-accuracy pavement crack segmentation. Automation in Construction, 162, 105375.
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