关键词: 棉花分割, YCbCr颜色空间, K均值算法, 粒子群算法

Abstract: Image segmentation of cotton is the key step of the cotton picker robot vision system. In the complex environment of the cotton fields of the strong light, shadow, etc. accurately and effectively splitting cotton, helps to determine its position in three-dimensional space. In accordance with the characteristics of cotton pictures, a method of Particle Swarm Optimization（PSO） and K-means hybrid clustering in YCbCr color space is proposed. This approach reinforces the exploitation of global optimum of the PSO algorithm. In order to avoid the premature convergence and speed up the convergence, traditional K-means algorithm is used to explore the local search space more efficiently dynamically according to the variation of the particle swarm’s fitness variance. The experiment results show that this method can segment cotton image with the complex background, and is more effective than the traditional PSO and K-means algorithm.

Key words: cotton segmentation, YCbCr color space, K-means algorithm, Particle Swarm Optimization（PSO） algorithm