基于免疫参数优化的汽车颜色识别方法

Vehicle color recognition approach based on immune parameter optimization

针对汽车颜色识别中的不确定性问题,通过引入模糊逻辑实现HSV颜色空间中各颜色特征分量的模糊化,将各分量的隶属度加权融合以进行颜色分类。为进一步提高识别算法的正确率和自适应能力,借鉴克隆选择、高频变异和免疫浓度调节等免疫机理,提出了一个基于免疫原理的参数优化算法,通过交叉、变异、克隆变异和选择等算子的作用,在抗体种群的免疫进化过程中实现对相关识别参数的优化学习。在不同测试集上与其他方法进行了对比实验,结果表明本文方法取得了更好的识别性能。

To address the uncertainty of vehicle color recognition,the fuzzy logic was introduced to implement fuzzification on three components of the hue,saturation and value（HSV） color space.Then the three fuzzy maps were fused to perform color classification.To further improve accuracy and adaptability of the recognition algorithm,an immune-based parameter optimization algorithm was proposed,borrowing ideas from the biological immune system,such as clone selection,hypermutation and concentration adjustment.Through the operators of crossover,mutation,clone mutation and selection,the corresponding recognition parameters were learnt from training samples through the process of immune evolutionary.Experiments were conducted on different data sets,compared with some traditional approaches.And the results show that the proposed approach achieves better performance.