Road crack detection based on improved U-Net

Jiaxing Du

doi:10.1117/12.2684288

1 August 2023 Road crack detection based on improved U-Net

Jiaxing Du

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541J (2023) https://doi.org/10.1117/12.2684288
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

The construction of road infrastructure equipment is being vigorously developed throughout the country and an improved UNet network model is After a long period of use, the pavement problems become more and more obvious, and the emergence of neural networks has substantially reduced labour costs and improved detection efficiency. After a long period of use, the pavement problems become more and more obvious, and the emergence of neural networks has substantially reduced labour costs and improved detection efficiency. One of them is UNet, an end-to-end deep learning network with excellent recognition capability for school object detection, so it can be applied to road crack defect detection. The paper improves the coding part on the traditional UNet network structure into a residual network ResNet50 and removes the connection layer and average pooling layer, using image greyscaling The paper improves the coding part on the traditional UNet network structure into a residual network ResNet50 and removes the connection layer and average pooling layer, using image greyscaling, image denoising and image enhancement to improve the recognition accuracy. The experimental results show that the proposed algorithm improves the accuracy by 2.1% and the IOU by 5.7% compared with the original UNet network.

Citation Download Citation

Jiaxing Du "Road crack detection based on improved U-Net", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541J (1 August 2023); https://doi.org/10.1117/12.2684288

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KEYWORDS

Image processing

Roads

Image segmentation

Feature extraction

Image enhancement

Object detection

Education and training

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