Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914510962425856 |
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| author | Xiao, Haosong Ramesh, Yamini Shukla, Rishabh Sarkar, Swarat He, Chaozhe R. |
| author_facet | Xiao, Haosong Ramesh, Yamini Shukla, Rishabh Sarkar, Swarat He, Chaozhe R. |
| contents | In this paper, we report the world's first infrastructure-guided communication-enhanced road crack detection pipeline that is effective and implementable on passenger vehicles. We first design a customized communication protocol to transmit the region of interest from the infrastructure to the vehicle. With proper camera image processing (e.g., dynamic cropping and frame selection), the focused images are provided to the crack detection model. Leveraging state-of-the-art crack detection model backbones and a carefully prepared dataset comprising a forward-facing view with a crack, we train the model to improve crack-detection performance. We demonstrate the full detection pipeline on an experimental vehicle platform, showcase the detection effectiveness, and project future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24616 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation Xiao, Haosong Ramesh, Yamini Shukla, Rishabh Sarkar, Swarat He, Chaozhe R. Computer Vision and Pattern Recognition In this paper, we report the world's first infrastructure-guided communication-enhanced road crack detection pipeline that is effective and implementable on passenger vehicles. We first design a customized communication protocol to transmit the region of interest from the infrastructure to the vehicle. With proper camera image processing (e.g., dynamic cropping and frame selection), the focused images are provided to the crack detection model. Leveraging state-of-the-art crack detection model backbones and a carefully prepared dataset comprising a forward-facing view with a crack, we train the model to improve crack-detection performance. We demonstrate the full detection pipeline on an experimental vehicle platform, showcase the detection effectiveness, and project future research directions. |
| title | Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.24616 |