农业自动化机械障碍物检测研究进展  

Research progress of agricultural automatic machinery obstacle detection

作　　者：成科扬[1,2,3] 朱雪森 裴运申 詹永照 CHENG Keyangg;ZHU Xuesen;PEI Yunshen;ZHAN Yongahao(School of Computer Science and Telecommunications Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center,Zhenjiang,Jiangsu 212013,China;Cyber Space Security Academy,Jiangsu University,Zhenjiang,Jiangsu 212013,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]江苏省大数据泛在感知与智能农业应用工程研究中心,江苏镇江212013 [3]江苏大学网络空间安全研究院,江苏镇江212013

出　　处：《江苏大学学报（自然科学版）》2023年第4期415-425,共11页Journal of Jiangsu University：Natural Science Edition

基　　金：国家自然科学基金资助项目(61972183)。

摘　　要：农业机械自动导航技术在智慧农业领域饱受关注,而障碍物检测则是其中的重要环节.首先分析了早年传感器检测技术的不足,然后对计算机视觉应用于农业机械智能障碍物的检测方法以及应用前景进行了综述.传感器技术由单一传感器到多传感器信息融合,虽然已经十分成熟,但仍存在受障碍物表面影响以及检测成本过高等不足.计算机视觉和深度学习在近两年不断被用于农业领域,例如卷积神经网络等,但在遮挡、远距离检测、移动障碍物检测等多个方面仍有很大的提升空间.对二十几年来的农业障碍物检测技术进行了概括,总结了现有问题,并提出了符合未来我国智慧农业发展的新思路.Automatic navigation technology of agricultural machinery is concerned in the field of intelligent agriculture,while the obstacle detection is an important part.The shortcomings of sensor detection technology in the early years were analyzed,and the application and application prospects of computer vision in agricultural machinery intelligent obstacle detection methods were summarized.The results show that sensor technology is now developed from single sensor to multi-sensor fusion at very mature stage,but it is still affected by obstacles surface and too high detection cost.In recent years,computer vision and deep learning have been used in agriculture of convolutional neural network,but it should be improved much in occlusion,remote detection,moving obstacle detection and other aspects.According to the agricultural obstacle detection techniques in the past 20 years,the existing problems are summarized,and a new idea for the future development of smart agriculture in China is proposed.

关 键 词：农业机械 障碍物检测 计算机视觉 传感器 信息融合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]