满足RoboCup规范小型类人足球机器人设计与目标识别  

Design of kid size humanoid soccer robot meeting the RoboCup specification and its object detection

作　　者：燕必希[1] 王智 费奇 王子润 董明利[1] YAN Bixi;WANG Zhi;FEI Qi;WANG Zirun;DONG Mingli(School of Instrument Science and Optoelectronic Engineering,Beijing Information Science & Technology University, Beijing 100192,China)

机构地区：[1]北京信息科技大学仪器科学与光电工程学院,北京100192

出　　处：《北京信息科技大学学报（自然科学版）》2019年第2期1-4,共4页Journal of Beijing Information Science and Technology University

基　　金：国家高技术研究发展计划(863计划)(2015AA043208);北京信息科技大学2018年人才培养质量提高项目(5111823205)

摘　　要：设计了符合RoboCup技术规范的小型类足球机器人,并通过深度学习算法YOLO对比赛场地的目标进行训练和识别。分析了RoboCup技术规范对小型类人机器人设计的要求,设计并加工装配了满足要求的机器人。应用3000幅现场图片对深度学习神经网络进行训练,并对300幅不同于训练样本的图片进行识别,识别准确率为93. 8%,召回率为89. 7%,检测速度可达40 f/s。实验结果表明深度学习算法YOLO相比于其它算法应用于RoboCup足球机器人具有较好的鲁棒性和实时性。A kid size soccer humanoid robot which meets RoboCup technical specification is designed, and the deep learning algorithm YOLO is trained to recognize the targets of the match field. The requirements of RoboCup technical specification for the design of small humanoid robot are analyzed, and the robot that meets the requirements is designed, processed and assembled. YOLO is trained with 3000 pictures of field objects and other 300 different pictures from training ones are identified. The experimental results show that the accuracy of the algorithm is 93.8%;the recall rate is 89.7%, and the detection speed can reach 40 frames per second. It shows that the deep learning algorithm YOLO has better robustness and real-time performance than other algorithms applied to RoboCup soccer robot in object detection.

关 键 词：ROBOCUP YOLO 目标识别 鲁棒性 实时性 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]