College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Sensors (Basel). 2022 Feb 18;22(4):1617. doi: 10.3390/s22041617.
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
这项工作主要研究农业自动化中蔬菜的非接触式测量问题。随着信息技术和人工智能的快速发展，计算机视觉在辅助农业生产中的应用显著提高了工作效率。本文基于目标检测和立体相机，提出了一种用于蔬菜识别和尺寸估计的智能方法。该方法使用双目立体相机获取彩色图像和深度图。然后，检测网络对四种常见蔬菜（黄瓜、茄子、番茄和辣椒）进行分类，并为每个物体定位六个关键点。最后，使用关键点的像素位置和深度计算蔬菜的大小。实验结果表明，该方法可以在 60 厘米内对四种常见蔬菜进行分类，并准确估计它们的直径和长度。本工作为解决蔬菜的非接触式测量问题提供了新的思路，能够推动计算机视觉在农业自动化中的应用。
