A deep learning-based adaptive segmentation method for general, national, and provincial trunk roads

Qian Dang; Kejun Jin; Siyi Wei; Bin Xie

doi:10.1117/12.2686761

10 August 2023 A deep learning-based adaptive segmentation method for general, national, and provincial trunk roads

Qian Dang, Kejun Jin, Siyi Wei, Bin Xie

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275920 (2023) https://doi.org/10.1117/12.2686761
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

This paper proposed an adaptive road segmentation method based on deep learning for the typical features of variable video scenes and unfixed monitoring perspectives of general national and provincial trunk highways, and constructed an adaptive road segmentation algorithm based on Unet-VGG16 by comparing backbone feature extraction networks to achieve intelligent extraction of variable scenes and perspectives of highway pavement area. The detection accuracy of the algorithm was 95.77% and the recall rate was 96.32%, which achieved good detection performance.

Citation Download Citation

Qian Dang, Kejun Jin, Siyi Wei, and Bin Xie "A deep learning-based adaptive segmentation method for general, national, and provincial trunk roads", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275920 (10 August 2023); https://doi.org/10.1117/12.2686761

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KEYWORDS

Roads

Image segmentation

Video surveillance

Semantics

Feature extraction

Video

Data modeling

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