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    This study introduces an efficient algorithm for detecting road potholes using stereo vision. The method accurately identifies potholes by analyzing road disparity maps, improving road safety and maintenance.

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    Area of Science:

    • Computer Vision
    • Road Infrastructure Monitoring
    • Machine Learning

    Background:

    • Potholes are a prevalent form of road damage impacting safety and vehicle condition.
    • Manual pothole detection by inspectors is hazardous and time-consuming.

    Purpose of the Study:

    • To develop an efficient and accurate pothole detection algorithm using computer vision techniques.
    • To automate the process of identifying road damage, reducing manual labor and risks.

    Main Methods:

    • Stereo vision-based road disparity map estimation incorporating rig roll angle.
    • Semiglobal matching for efficient disparity calculation.
    • Disparity map transformation and simple linear iterative clustering (SLIC) for superpixel segmentation.
    • Adaptive thresholding for pothole identification based on superpixel intensity.

    Main Results:

    • The algorithm successfully distinguishes damaged road areas by analyzing transformed disparities.
    • Potholes are accurately detected by identifying low-intensity superpixels.
    • Implementation on NVIDIA RTX 2080 Ti GPU using CUDA demonstrates high efficiency.

    Conclusions:

    • The proposed algorithm achieves state-of-the-art accuracy and efficiency in pothole detection.
    • This automated approach offers a safer and faster alternative to manual inspection methods.
    • The technique has significant potential for real-time road condition monitoring and infrastructure management.