基于YOLO算法的果蔬检测

S. Kanakaprabha, Dr. Gaddam Venu Gopal, D. Kaleeswaran, D. Hemamalini, Dr. G. Ganeshkumar
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引用次数: 0

摘要

机器人收获平台的果蔬检测系统至关重要。由于枝叶移动、日照、果蔬丛、阴影等不均匀的环境因素,使得水果识别变得更加困难。目前在这项工作中使用的方法是检测不同大小和形状的不同类型的水果和蔬菜。该方法使用了OpenCV, Dark Flow, YOLO技术的TensorFlow变体。为了训练网络的必要条件,将一系列水果和蔬菜图片输入到网络中。在进入训练之前,使用OpenCV对照片进行预处理,以在水果和蔬菜周围创建手动边框。采用YOLO检测算法。该方法能更准确、快速地识别图像中的物体。网络训练完成后,将测试输入发送到识别的水果和蔬菜周围的边界框中,结果将显示出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fruits and Vegetables Detection using YOLO Algorithm
The robotic harvesting platform's fruit and vegetable detection system is crucial. Due to uneven environmental factors such branch and leaf shifting sunshine, fruit and vegetable clusters, shadow, and so on, the fruit recognition has become more difficult in nowadays. The current method in this work is used to detect different types of fruits and vegetables in different size and shape. This method makes the use of OpenCV, Dark Flow, a TensorFlow variant of the YOLO technique. To train the necessary of network, a range of fruits and vegetable pictures were input into the network. The photos were pre-processed using OpenCV to create manual bounding boxes around the fruits and vegetables before into the training. YOLO detection algorithm is used. In, this method more accurately and rapidly recognizes of an item in an image. After the network has been trained, the test input is sent into the bounding boxes surrounding the recognized fruits and vegetables will be displayed as a consequence.
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