Color Recognition in Challenging Lighting Environments: CNN Approach
This addresses a domain-specific problem in computer vision for applications requiring reliable color detection in challenging lighting, but it is incremental as it builds on existing CNN approaches.
The paper tackles color recognition under varying lighting conditions by proposing a CNN-based method that first segments objects using edge detection, then classifies their colors, achieving improved robustness and better performance than existing methods.
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.