mlsanityData to 7 Oct 2026

Color Recognition in Challenging Lighting Environments: CNN Approach

arXiv 2402.04762 ↗First posted 7 Feb 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques for the application of computer vision. They have implemented proposed several methods using different color detection approaches but still, there is a gap that can be filled. To address this issue, a color detection method, which is based on a Convolutional Neural Network (CNN), is proposed. Firstly, image segmentation is performed using the edge detection segmentation technique to specify the object and then the segmented object is fed to the Convolutional Neural Network trained to detect the color of an object in different lighting conditions. It is experimentally verified that our method can substantially enhance the robustness of color detection in different lighting conditions, and our method performed better results than existing methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 7 Feb 2024.

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5 of 27 references.

References · 5

  1. 2017MSR-net:Low-light Image Enhancement Using Deep Convolutional Networkcited by 43
  2. 2015Color Constancy Using CNNscited by 21
  3. 2020Deep Lighting Environment Map Estimation from Spherical Panoramascited by 9
  4. 2015Vehicle Color Recognition using Convolutional Neural Networkcited by 4
  5. 2021Spatially and color consistent environment lighting estimation using deep neural networks for mixed realitycited by 1

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