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13 September 2024 Pavement crack detection with multi-scale feature extraction and deep supervised feature fusion
Wen Li Zhang, Zhong Qu
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Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 132540O (2024) https://doi.org/10.1117/12.3039123
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China
Abstract
In order to ensure the safety of the road surface, it is very important to detect the cracks in time and accurately. This paper proposes a pavement crack detection network model based on multi-scale feature extraction and deep supervised feature fusion. Firstly, multi-scale separable convolution blocks are used to extract crack features, which makes the model more effectively model pavement cracks with different topological structures. Then, the obtained feature maps are added to the deep supervised network to aggregate multi-level features, so that the model can converge faster and better. Finally, in order to make full use of the lateral output feature information of each stage, the dynamic feature fusion method is used to realize the efficient integration of multi-level features, which significantly optimizes the completeness and accuracy of the final prediction map. According to the evaluation results on DeepCrack, CFD and Crack500 public datasets, the proposed method shows better performance than other methods in crack detection.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Wen Li Zhang and Zhong Qu "Pavement crack detection with multi-scale feature extraction and deep supervised feature fusion", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 132540O (13 September 2024); https://doi.org/10.1117/12.3039123
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KEYWORDS
Feature fusion
Convolution
Feature extraction
Network architectures
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