摘要
由于智能小车探测周围环境的硬件设备的繁杂,将卷积神经网络与摄像头结合来探测周围环境越来越成为研究的热点。然而,单纯地使用卷积神经网络处理摄像头的数据来控制小车的转角,存在训练时间久、准确率不高的问题。针对上述问题,该文提出了将摄像头的数据经过无监督的二分K-means聚类方法之后,再将聚类结果作为卷积神经网络的输入,最终预测小车转角。实验结果证明,该网络结构可以有效地提高网络的训练速度,并提高网络的准确率。
As the intelligent little vehicle’s hardware equipments for detecting the surrounding environment are too complex,it has become a research hotspot to combine convolutional neural network with camera to detect the surrounding environment.However,if the convolutional neural network is applied alone to process the data of the camera for the turning angle control of the little vehicle,the training time would be long and the accuracy would be low.Based on the above problems,the camera data is processed by means of the unsupervised binary k-means clustering method,and then the clustering result is taken as the input of the convolutional neural network,so as to predict the little vehicle’s turning angle.The Experiment results show that the network structure can effectively improve the training speed and accuracy of the network.
作者
曾明如
杨向文
祝琴
ZENG Mingru;YANG Xiangwen;ZHU Qin(School of Information Engineering,Nanchang University,Nanchang 330031,China)
出处
《现代电子技术》
北大核心
2020年第6期140-143,共4页
Modern Electronics Technique
基金
国家自然科学基金(71563028)。
关键词
转角预测
卷积神经网络
数据处理
周围环境探测
网络训练
结果分析
turning angle prediction
convolutional neural network
data processing
surrounding environment detection
network training
result analysis
