Color Recognition of Vehicle Based on Low Light Enhancement and Pixel-wise Contextual Attention

At present, as a direction of intelligent transportation, the research results of car body color detection are still relatively lacking, and the current car body color detection is still easy to be affected by light, shielding, pollution and other factors. This paper proposes a color recognition of vehicle based on low light enhancement and Pixel-wise Contextual Attention, including low light intensity enhancement based on dual Fully Convolutional Networks (FCN), vehicle body detection based on Pixel-wise Contextual Attention Networks (PiCANet), and color classification of vehicle based on Convolutional Neural Network (CNN). The method of low light enhancement has better robustness and adaptability, and can better process the dark image. We use Pixel-wise Contextual Attention Networks, which better identify main area of vehicle with context information. Experiments show that our method is more accurate than the state-of-the-art method with 0.6% under insufficient light.