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OCDBMamba: A Robust and Efficient Road Pothole Detection Framework with Omnidirectional Context and Consensus-Based

Feng Ling1, Yunfeng Lin1, Weijie Mao2

  • 1College of Engineering, Lishui University, Lishui 323000, China.

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Summary

OCDBMamba enhances road pothole detection by integrating omnidirectional context modeling and consensus-driven boundary selection. This framework improves localization accuracy and robustness in challenging road conditions.

Keywords:
dual-branch consensus thresholdinglong-range context modelingomnidirectional channel-selective scanningroad pothole detectionstate space modeling

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Road pothole detection is crucial for infrastructure maintenance and autonomous driving.
  • Existing methods struggle with complex environments, leading to false alarms and boundary instability.

Purpose of the Study:

  • To develop a unified and efficient framework for robust road pothole detection.
  • To address challenges like low contrast, shadows, water films, and background textures.

Main Methods:

  • Proposed OCDBMamba framework with Omnidirectional Channel-Selective Scanning (OCS) for long-range structural cue aggregation.
  • Introduced Dual-Branch Consensus Thresholding (DBCT) for branch-level agreement and boundary consistency.

Main Results:

  • Achieved 90.7% mAP50 and 67.8% mAP50:95 on diverse datasets.
  • Demonstrated high precision (0.905) and recall (0.812) with 13.1 GFLOPs.
  • Outperformed YOLOv11n by 5.4% and 5.6% in key metrics.

Conclusions:

  • OCDBMamba offers stable localization and enhanced robustness in varied road conditions.
  • The synergy of OCS and DBCT validates its effectiveness for practical road inspection and perception.