Abstract
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.
Abstract (translated)
光在无论是人还是机器视觉中扮演着至关重要的角色，感知到的颜色始终基于周围环境的照明条件。研究人员正在努力增强计算机视觉应用中的颜色检测技术。他们提出了几种使用不同颜色检测方法实现的方法，但仍存在一个可以填补的空白。为了解决这个问题，我们提出了一个基于卷积神经网络（CNN）的颜色检测方法。首先，使用边缘检测分割技术进行图像分割，以便指定物体，然后将分割的物体输入到训练以检测不同光照条件下物体颜色的卷积神经网络中。经过实验验证，我们的方法可以在不同光照条件下显著增强颜色检测的鲁棒性，并且我们的方法的表现优于现有方法。
URL
https://arxiv.org/abs/2402.04762
