Automated Pavement Distress Detection Based on Convolutional Neural Network

Pavement distress detection is crucial in road health assessment and monitoring. However, there are still some challenges in extracting pavement distress based on deep learning: such as insufficient segmentation, extraction errors and discontinuities. In this paper, we propose DARNet, a network for...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.105055-105068
Hauptverfasser:	Zhang, Jinhe, Sun, Shangyu, Song, Weidong, Li, Yuxuan, Teng, Qiaoshuang
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adaptation models Artificial neural networks attention mechanisms convolutional neural network Convolutional neural networks Damage Data mining Datasets Decoding Defect detection Discontinuity Entropy Feature extraction Modules multiscale feature fusion pavement distress baseline dataset Pavement distress detection Pavements Pixels Roads Surface cracks
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