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Pavement Disease Detection and Segmentation

Published: 28 February 2024 Publication History

Abstract

Pavement disease identification based on target detection and image segmentation is a common method in the maintenance of pavement diseases, which has great study value for the repair of road disease and road health status assessment. The study use pavement disease as the target, building segmention data set and detection data set which contains pothole, lateral crack, longitudinal crack and mesh crack. For the detection task, the pavement disease detection model is based on the improved YOLOv5, and the model’s mAP is 94.2%. For the segmentation task, pavement disease segmentation model is based on the improved U-Net, and the model’s mIOU is 94.4%.

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ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
October 2023
589 pages
ISBN:9798400707988
DOI:10.1145/3633637
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 February 2024

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Author Tags

  1. U-Net
  2. YOLOv5
  3. convolutional neural network
  4. pavement disease

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  • Refereed limited

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  • Basic Research Program of Qinghai Province

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ICCPR 2023

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