基于BP神经网络的小型组足球机器人视觉子系统的图像分割技术

BP neural network based image segmentation technique for small sized league soccer robot team system

为解决小型足球机器人视觉子系统图像分割的实时性和光照适应性问题,将BP神经网络技术应用到图像分割中。在图像分割技术和BP神经网络的理论分析基础上,建立了两者之间的关系,并建立了相应的BP神经网络模型。图像像素离散化并将其H、Cb、Cr分量值作为神经网络的输入,将目标像素点分类类别作为神经网络的输出。通过改进神经网络学习率参数和误差计算函数提高了神经网络的收敛速度,采用背景相减法和归一化处理算法提高了系统的实时性。实验结果表明所设计的BP神经网络模型和学习算法能够有效地解决小型足球机器人视觉子系统图像分割实时性和光照适应性等问题。

BP neural network was adopted in the small sized league（SSL） robot soccer team for image segmentation in order to fulfill the requirements of real time processing and environmental light adaptation.The theoretical relationship between the image segmentation and BP neural network was studied,and the model of the BP neural network was established.The array of each component values（H,Cb,Cr） of all the pixels in the image were taken as the input of neural network,and the arrays of target pixel categories as the output.The convergence rate was improved by optimizing the learning ratio parameters and the error function definition.The background-subtraction method and normalization algorithms were applied to speed the segmentation process.The image segmentation experiment shows that the model and algorithm of BP neural network can solve the image segmentation problems for small size soccer robot vision subsystem,and demonstrate good performance in real timing and adaptability in different lighting conditions.