# Vehicle Color Recognition using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1510.07391 · HTML: https://mlsanity.com/paper/1510.07391/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2015.

## Built on by (most cited first, 4 of 4)

- [Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces](https://mlsanity.com/paper/2305.00002/) (2023)
- [Veri-Car: Towards Open-world Vehicle Information Retrieval](https://mlsanity.com/paper/2411.06864/) (2024)
- [Color Recognition in Challenging Lighting Environments: CNN Approach](https://mlsanity.com/paper/2402.04762/) (2024)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)

## Abstract (arXiv)

Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The training process follow procedure introduce by Krizhevsky, that learning rate is decreasing by factor of 10 after some iterations. To test our method, we use publicly vehicle color recognition dataset provided by Chen. The results, our model outperform the original system provide by Chen with 2% higher overall accuracy.