研究目的
To propose a novel crack detection system that uses a light field imaging sensor instead of a conventional 2D camera for improved crack detection in road pavement surfaces.
研究成果
The proposed light field crack detection system outperforms the 2D-based CrackIT system, with an average F-measure increase from 79% to 85%, showing that light field disparity information can improve crack detection by reducing false positives and handling pavement textures better. This demonstrates the potential of light field sensors for road surveys.
研究不足
The system uses simple image processing techniques and a small dataset of 13 images; future work could involve more sophisticated processing and a larger dataset. The disparity information is more abundant near the image center, and depth maps from the Lytro software were not helpful for crack detection.
1:Experimental Design and Method Selection:
The system uses a light field camera (Lytro Illum) to capture images, decodes them into a multi-view array of 2D sub-aperture images, computes disparity between image pairs, applies edge detection and filtering, and uses region linking for crack detection.
2:Sample Selection and Data Sources:
13 light field images captured from pavements with different surface textures using the Lytro Illum camera positioned 1 meter above the surface.
3:List of Experimental Equipment and Materials:
Lytro Illum camera, Matlab Light Field Toolbox v
4:4 for decoding, and a computer for processing. Experimental Procedures and Operational Workflow:
Image acquisition, decoding to 15x15 sub-aperture images, grayscale conversion, thresholding, disparity computation, edge detection, median filtering, region filtering, and connected component analysis.
5:Data Analysis Methods:
Block-based evaluation using precision, recall, and F-measure metrics compared to a ground truth labeled by an expert.
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