A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-scale feature fusion network for pixel-level pavement distress detection
Abstract Automatic pavement distress detection is essential to monitoring and maintaining pavement condition. Currently, many deep learning-based methods have been utilized in pavement distress detection. However, distress segmentation remains as a challenge under complex pavement conditions. In this paper, a novel deep neural network architecture, W-segnet, based on multi-scale feature fusions, is proposed for pixel-wise distress segmentation. The proposed W-segnet concatenates distress location information with distress classification features in two symmetric encoder-decoder structures. Three major types of distresses: crack, pothole, and patch are segmented and the results were discussed. Experimental results show that the proposed W-segnet is robust in various scenarios, achieving a mean pixel accuracy (MPA) of 87.52% and a mean intersection over union (MIoU) of 75.88%. The results demonstrate that W-segnet outperforms other state-of-the-art semantic segmentation models of U-net, SegNet, and PSPNet. Comparison of cost of model training and inference indicates that W-segnet has the largest number of parameters, which needs a slightly longer training time while it does not increase the inference cost. Four public datasets were used to test the generalization ability of the proposed model and the results demonstrate that the W-segnet possesses well segmentation performance.
Highlights W-segnet with two encoder-decoder architectures is proposed for multi type of pavement distresses segmentation. A challenging benchmark dataset was established via UAV for distress segmentation. W-segnet is applied in segmenting pavement crack, pothole, and patch images under various scenarios. W-segnet outperforms U-net, SegNet, and PSPNet in self-built and open-source dataset with higher accuracy. W-segnet possesses a higher inference speed for model deployment in practical application.
Multi-scale feature fusion network for pixel-level pavement distress detection
Abstract Automatic pavement distress detection is essential to monitoring and maintaining pavement condition. Currently, many deep learning-based methods have been utilized in pavement distress detection. However, distress segmentation remains as a challenge under complex pavement conditions. In this paper, a novel deep neural network architecture, W-segnet, based on multi-scale feature fusions, is proposed for pixel-wise distress segmentation. The proposed W-segnet concatenates distress location information with distress classification features in two symmetric encoder-decoder structures. Three major types of distresses: crack, pothole, and patch are segmented and the results were discussed. Experimental results show that the proposed W-segnet is robust in various scenarios, achieving a mean pixel accuracy (MPA) of 87.52% and a mean intersection over union (MIoU) of 75.88%. The results demonstrate that W-segnet outperforms other state-of-the-art semantic segmentation models of U-net, SegNet, and PSPNet. Comparison of cost of model training and inference indicates that W-segnet has the largest number of parameters, which needs a slightly longer training time while it does not increase the inference cost. Four public datasets were used to test the generalization ability of the proposed model and the results demonstrate that the W-segnet possesses well segmentation performance.
Highlights W-segnet with two encoder-decoder architectures is proposed for multi type of pavement distresses segmentation. A challenging benchmark dataset was established via UAV for distress segmentation. W-segnet is applied in segmenting pavement crack, pothole, and patch images under various scenarios. W-segnet outperforms U-net, SegNet, and PSPNet in self-built and open-source dataset with higher accuracy. W-segnet possesses a higher inference speed for model deployment in practical application.
Multi-scale feature fusion network for pixel-level pavement distress detection
Zhong, Jingtao (author) / Zhu, Junqing (author) / Huyan, Ju (author) / Ma, Tao (author) / Zhang, Weiguang (author)
2022-06-14
Article (Journal)
Electronic Resource
English
Pavement Distress Level Prediction Using Multi-PBANNS Techniques
| British Library Conference Proceedings | 1997