刘慧,张礼帅,沈跃,张健,吴边.基于改进SSD的果园行人实时检测方法[J].农业机械学报,2019,50(4):29-35,101.
LIU Hui,ZHANG Lishuai,SHEN Yue,ZHANG Jian,WU Bian.Real-time Pedestrian Detection in Orchard Based on Improved SSD[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(4):29-35,101.
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基于改进SSD的果园行人实时检测方法   [下载全文]
Real-time Pedestrian Detection in Orchard Based on Improved SSD   [Download Pdf][in English]
投稿时间:2018-12-29  
DOI:10.6041/j.issn.1000-1298.2019.04.003
中文关键词:  无人农业车辆  行人检测  单次多重检测器  空洞卷积  MobileNetV2
基金项目:国家自然科学基金项目(5150195)、江苏省国际科技合作项目(BZ2017067)、江苏省重点研发计划项目(BE2018372)、江苏省自然科学基金项目(BK20181443)、镇江市重点研发计划项目(NY2018001)和江苏高校青蓝工程项目
作者单位
刘慧 江苏大学 
张礼帅 江苏大学 
沈跃 江苏大学 
张健 江苏大学 
吴边 江苏大学 
中文摘要:农田障碍物的精确识别是无人农业车辆必不可少的关键技术之一。针对果园环境复杂难以准确检测出障碍物信息的问题,提出了一种改进单次多重检测器(Single shot multibox detector,SSD)深度学习目标检测方法,对田间障碍物中的行人进行识别。使用轻量化网络MobileNetV2作为SSD模型中的基础网络,以减少提取图像特征过程中所花费的时间及运算量,辅助网络层以反向残差结构结合空洞卷积作为基础结构进行位置预测,在综合多尺度特征的同时避免下采样操作带来的信息损失,基于Tensorflow深度学习框架,在卡耐基梅隆大学国家机器人工程中心的果园行人检测开放数据集上进行不同运动状态(运动、静止)、不同姿态(正常、非正常)和不同目标面积(大、中、小)的田间行人识别精度和识别速度的对比试验。试验表明,当IOU阀值为0.4时,改进的SSD模型田间行人检测模型的平均准确率和召回率分别达到了97.46%和91.65%,高于改进前SSD模型的96.87%和88.51%,并且参数量减少至原来的1/7,检测速度提高了187.5%,检测速度为62.50帧/s,模型具有较好的鲁棒性,可以较好地实现田间环境下行人的检测,为无人农机的避障决策提供依据。
LIU Hui  ZHANG Lishuai  SHEN Yue  ZHANG Jian  WU Bian
Jiangsu University,Jiangsu University,Jiangsu University,Jiangsu University and Jiangsu University
Key Words:unmanned agricultural vehicles  pedestrian detection  SSD  dilated convolution  MobileNetV2
Abstract:Reliable pedestrian detection in agriculture field is one of the key technologies for unmanned agricultural vehicles. For the complex environment in the orchard, it is difficult to accurately detect the obstacle information. To solve this problem, an improved single shot multibox detector (SSD) deep learning object detection method was proposed to detect pedestrian in the field obstacles. The lightweight network framework MobileNetV2 was used as the basic network in the SSD model to reduce the time and computational effort of extracting image features. For auxiliary layer of the SSD model, the inverse residual block combined with the dilated convolution was used as the basic structure for position prediction so that the multi-scale features can be integrated and at the same time avoiding the information loss caused by the down sampling operation. Based on the Tensorflow deep learning framework, different motion states (motion and static), different pose states (normal and unnormal) and different object scales (large, medium and small) pedestrian detection experiment in orchard were carried out on the open data set in orchard environment of the National Robotics Engineering Center of Carnegie Mellon University and the accuracy and speed of these different situations were compared. Result showed that the average precision and recall rate of the improved SSD pedestrian detection model in agriculture reached 97.46% and 91.65%, respectively, higher than 96.87% and 88.51% of the original SSD model, and the parameter quantity was decreased by seven times. The speed was accelerated by three times and the detection speed was 62.50 frames per second. The model had good robustness and could detect the pedestrian in the field environment, which could provide a basis for the obstacle avoidance of the unmanned agriculture machinery.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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