In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

中文翻译：

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羽毛球运动捕捉与采摘机器人的视觉图像检测

在草莓易碎水果采摘过程中，为了降低水果的破损率，提高采摘机器人的精度和效率，现场提出了一种基于国际标准的羽毛球边缘特征检测和捕捉自动化算法流程的动作捕捉系统夜间采摘机器人羽毛球动作捕捉技巧训练方法。羽毛球动作捕捉系统可以实时分析比赛视频，通过动作捕捉获取优秀羽毛球运动员的准确率和羽毛球动作捕捉的技术特点。本文的目的是将高精度动作捕捉视觉控制系统应用到机器人夜间拣选过程中的视觉控制系统设计中，从而有效提高机器人在夜间拣选过程中的观察和识别精度，从而提高作业的自动化程度。本文对采摘机器人视觉系统的可靠性进行了测试。以夜间采摘环境为例，对采摘机器人采摘的水果边缘特征进行图像处理。结果表明，平滑和增强的图像处理可以成功提取水果图像的边缘特征。通过边缘特征测试对目标识别率的准确性和拣选机器人视觉系统的定位能力进行了测试。结果表明，视觉系统的目标识别率准确率和运动边缘定位能力均远高于91%，