# Color Recognition in Challenging Lighting Environments: CNN Approach

arXiv: https://arxiv.org/abs/2402.04762 · HTML: https://mlsanity.com/paper/2402.04762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [MSR-net:Low-light Image Enhancement Using Deep Convolutional Network](https://mlsanity.com/paper/1711.02488/) (2017)
- [Color Constancy Using CNNs](https://mlsanity.com/paper/1504.04548/) (2015)
- [Deep Lighting Environment Map Estimation from Spherical Panoramas](https://mlsanity.com/paper/2005.08000/) (2020)
- [Vehicle Color Recognition using Convolutional Neural Network](https://mlsanity.com/paper/1510.07391/) (2015)
- [Spatially and color consistent environment lighting estimation using deep neural networks for mixed reality](https://mlsanity.com/paper/2108.07903/) (2021)

## 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.