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A Multi-Purpose Realistic Haze Benchmark With Quantifiable Haze Levels and Ground Truth.

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    Researchers created a new benchmark dataset for realistic haze images from aerial and ground views. This dataset aids in evaluating computer vision algorithms for object recognition in degraded visual environments (DVE).

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • Outdoor imagery is often degraded by smoke or haze, impacting scene understanding.
    • Existing datasets lack realism and comprehensive data for evaluating computer vision in degraded visual environments (DVE).

    Purpose of the Study:

    • Introduce the first realistic haze image benchmark dataset.
    • Provide paired haze-free and hazy images with in-situ haze density measurements.
    • Facilitate evaluation of computer vision algorithms in DVE.

    Main Methods:

    • Generated realistic haze using professional smoke machines in a controlled environment.
    • Captured images from both unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) perspectives.
    • Collected paired haze-free images and in-situ haze density measurements.

    Main Results:

    • Developed a novel benchmark dataset for DVE research.
    • Dataset includes ground truth object classification bounding boxes and haze density.
    • Evaluated state-of-the-art dehazing and object detection algorithms on the dataset.

    Conclusions:

    • The introduced dataset addresses the need for realistic benchmarks in DVE research.
    • The dataset enables robust evaluation of computer vision algorithms under haze conditions.
    • The dataset is publicly available for community use and algorithm development.