基于YOLOv3的油茶果视觉定位系统

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...

Visual Positioning System for Camellia Oleifera Fruit Based on YOLOv3

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.