基于数据平衡深度学习的不同成熟度冬枣识别

Recognition Approach Based on Data-balanced Faster R-CNN for Winter Jujube with Different Levels of Maturity

为解决不同成熟度冬枣的样本数量相差悬殊导致的识别率低的问题,本文提出了一种基于数据平衡的Faster R-CNN的冬枣识别方法。该方法针对自然环境下不同成熟度的冬枣,首先从不同角度进行了数据平衡的Faster R-CNN冬枣识别方法研究,然后将所提出的方法与基于YOLOv3的识别方法进行了对比试验研究。研究结果表明:所提出的数据平衡的Faster R-CNN方法在样本数量不足和类别不平衡的情况下,增强了模型的泛化效果,对片红冬枣识别的平均精确度达到了98.50%,总损失值小于0.5,其识别平均精确度高于YOLOv3。该研究对解决冬枣自动化和智能化采摘的识别问题具有一定的实际意义和应用价值。

Winter jujube has the characteristics of thin peel and crisp flesh,and winter jujube can only be picked by hand at present,so it is urgent to solve the problem of automatic and intelligent picking of winter jujube.Whereas,the recognition of winter jujube is the premise and foundation to solve this problem.In order to solve the problem of low recognition rate caused by the large number difference of samples with different levels of maturity,this paper proposes a recognition approach based on data-balanced Faster R-CNN for winter jujube.For the winter jujube with different levels of maturity in natural environment,this paper researches the Faster R-CNN recognition approach with data balance from different angles,and then the proposed method is compared with the recognition approach based on YOLOv3.The results show that:the proposed data-balanced Faster R-CNN method enhances the generalization effect of the model in the case of insufficient samples and unbalanced categories;the average recognition accuracy of the proposed approach is 98.50%which is higher than YOLOv3,and the total loss is less than 0.5.What's more,the feature extraction of the foreground image is not obvious because the distance is far between the lens and the foreground image,which will reduce the recognition accuracy of the overall data set.This research has certain practical significance and application value for solving the recognition problem of automatic and intelligent picking winter jujube.