基于YOLOv3的油茶果视觉定位系统  

Visual Positioning System for Camellia Oleifera Fruit Based on YOLOv3

作　　者：熊仕琦 王长坤[1] 熊璐康 XIONG Shi-Qi;WANG Chang-Kun;XIONG Lu-Kang(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学信息工程学院,南昌330063

出　　处：《计算机系统应用》2022年第1期132-137,共6页Computer Systems & Applications

基　　金：江西省自然科学基金(AA201920039)。

摘　　要：随着科技的进步,采摘机器人各个部分的系统也日益完善.其中,机器人视觉定位的系统设计很大程度影响了其工作效率,尤其是在目标检测速率、采摘果实准确率以及采摘目标环境适应度方面.本次研究提出利用双目立体视觉系统获取油茶果目标图像,并采集计算深度信息,制作自己的油茶果VOC数据集,采用YOLOv3目标检测算法来实现复杂环境下油茶果果实的识别,并通过设计上位机界面,直观展示对油茶果目标的定位功能.实验发现该方法具有更高的识别率和更快的识别速度,在复杂环境下展示了其算法的优越性.With the advancement of technology, the systems of various parts of the picking robots have been increasingly improved. The design of the visual positioning system largely affects the work efficiency of a picking robot, especially its target detection speed, fruit picking accuracy, and target picking environment adaptation. In this study, we propose to use a binocular stereo vision system to acquire images of camellia oleifera fruit targets and then collect and calculate depth information to build our own VOC dataset of Camellia oleifera fruits. The you only look once v3(YOLOv3) target detection algorithm is adopted to achieve Camellia oleifera fruit recognition in complex environments. The function of locating Camellia oleifera fruit targets is intuitively demonstrated by a newly designed upper computer interface.Experimental results show that compared with other methods, the proposed method has a higher recognition rate and a faster recognition speed, which demonstrates the superiority of its algorithm in complex environments.

关 键 词：双目立体视觉系统 YOLOv3 VOC数据集 油茶果识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S776.2[自动化与计算机技术—计算机科学与技术]