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System design of vehicle road crack identification

Published: 18 November 2024 Publication History

Abstract

In order to solve the problems of low efficiency and low accuracy of traditional manual detection of road cracks, this paper proposes a vehicle-mounted road crack recognition system based on machine vision and deep learning. It mainly includes three parts: pavement collection and sample set preparation, crack identification model training and crack detection. First, the road surface images are collected through the dual cameras on the vehicle, and image stitching and image enhancement techniques are used for preprocessing to reduce the effects of lighting, vibration, etc. The real road images are combined with the generative adversarial network (GAN) to expand and enrich the sample library, so as to build the customized sample set required for this project. Then, the U-Net convolutional neural network can exclude the non-road parts such as the front vehicle and green belt in the image, and only retain the road surface image, and the model is trained by the YOLOv8 target detection algorithm to obtain the road surface crack recognition model. Finally, the system can detect road surface cracks during vehicle driving, and accurately locate the crack position through the vehicle-mounted satellite positioning system, providing a scientific basis for road maintenance and quality assessment. The system not only improves detection efficiency, but also helps optimize the scheduling of road maintenance resources, and has significant social and economic value.

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ICCIR '24: Proceedings of the 2024 4th International Conference on Control and Intelligent Robotics
June 2024
399 pages
ISBN:9798400709937
DOI:10.1145/3687488
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: 18 November 2024

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Author Tags

  1. Design
  2. Generative Adversarial Network
  3. Image preprocessing
  4. Road crack

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ICCIR 2024

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Overall Acceptance Rate 131 of 239 submissions, 55%

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