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基于YOLOv5的核桃品种识别与定位

来源：花匠小妙招时间：2024-12-26 21:50

摘要： 为实现对不同品种核桃的分类与定位，提出一种基于深度学习的核桃检测方法。首先，以新疆南疆地区主产的三种核桃为对象进行图像采集，并对图像进行翻转、裁剪、去噪、光照变换等操作制作核桃数据集；然后，采用基于YOLOv5的检测模型进行试验，并与YOLOv3、YOLOv4和Faster RCNN算法进行比较。结果表明，基于YOLOv5的模型对新2、新光和温185核桃检测的平均精度均值分别为99.5%、98.4%和97.1%，单幅图像检测耗时为7 ms。在相同数据集、相同试验环境下，该模型的检测速度是Faster RCNN的7倍，该模型的检测精度比YOLOv4高2.8%且模型大小仅为YOLOv4的1/14。试验结果表明，基于YOLOv5的核桃检测方法在检测精度和速度上是所有对比算法中最高的，适合本研究的检测需求，可为机器人自主分拣核桃提供研究基础。

关键词: 深度学习, 核桃检测, YOLOv5, 自主分拣

Abstract: In order to classify and locate different walnut varieties, a walnut detection method based on deep learning was proposed. First of all, this paper took the three kinds of walnut mainly produced in the southern Xinjiang region as the object for image acquisition and made the walnut data set by flipping, clipping, denoising, lighting transformation, and other operations of the image. Then, the YOLOv5based detection model was used for experiments and compared with the detection results of YOLOv3, YOLOv4, and Faster RCNN algorithms. The results showed that the mean average accuracy (mAP) of the YOLOv5 model for walnut detection of Xin2, Xinguang, and Wen185 was 99.5%, 98.4%, and 97.1%, respectively, and the detection time of a single image was 7 ms. Under the same data set and the same experimental environment, the detection speed of the model is 7 times that of Faster RCNN, the detection accuracy of the model is 2.8% higher than that of Yolov4, and the model size is only 1/14 of that of YOLOv4. The test results showed that the model based on YOLOv5 of walnut detection was the highest in terms of detection accuracy and speed among all the comparison algorithms, which was suitable for the detection requirements of this research, which could provide a research basis for robot automatic walnut sorting.

Key words: deep learning, walnut detection, YOLOv5, automatic sorting

中图分类号:

S24