多层感知器自监督在线学习非结构化道路识别

针对智能车辆非结构化道路识别中存在的环境自适应性和在线学习算法实时性问题，提出了一种结合多线程技术和多层感知器自监督在线学习技术的道路识别算法. 通过识别结果在线自动更新训练集，并利用评估函数判断是否触发重训分类器，确保当前分类器对行驶道路环境的有效识别. 同时，算法中道路图像采集、分类器训练、训练集更新、分类器识别等计算操作分别在各自线程中实现，利用信号量对数据流进行同步互斥，优化计算资源，充分利用了多层感知器分类计算快的特点，并克服其训练耗时问题. 实际道路检测实验结果表明，算法具有较好的自适应性及实时性，能够满足智能车辆非结构化道路导航需求. 

Unstructured Road Recognition Using Self-Supervised Multilayer Perceptron Online Learning Algorithm

A self-supervised multilayer perceptron online learning algorithm was proposed to improve the adaptability and real-time performance for unmanned ground vehicle unstructured road recognition. The road recognition results were used to update the training data set characteristic vector, and an evaluation function was created to trigger classifier retraining, as a result, the current classifier can recognize the road surface efficiently. Also, in the algorithm the processing operations such as the road surface image data sampling, classifier training, training data set updating and classifier recognition were calculated in their own threading. The structure can take advantage of faster classification calculation character of multilayer perceptron, and overcome its problem of time consuming training process. The real vehicle road recognition tests show that the proposed algorithm has a better adaptability and can meet the real-time requirements of unmanned ground vehicle unstructured road navigation.