温室樱桃番茄果实深度学习识别模型研究

Research on deep learning recognition model of greenhouse cherry tomato fruit

【目的】选取樱桃番茄果实深度学习识别模型,实现对温室樱桃番茄果实快速、准确的检测。【方法】采集樱桃番茄果实样本,标注数据,构建数据集,对YOLOv4和YOLOv4-Tiny算法模型进行训练,并对训练得到的模型进行精确率、召回率、平均准确率、综合评价指标F1值的分析。【结果】在训练好的YOLOv4和YOLOv4-Tiny算法模型上,樱桃番茄果实识别预测准确率分别为100%和96.84%、召回率分别为91.12%和90.53%、平均精确率分别为96.32%和94.18%、综合评价指标F1值分别为0.95和0.94。【结论】YOLOv4算法模型明显优于YOLOv4-Tiny,能够实现对樱桃番茄果实目标的准确检测。

【Objective】Select deep learning cherry tomato fruit recognition models to achieve rapid and accurate detection of greenhouse cherry tomato fruits.【Methods】The training data set was constructed by collecting the cherry tomato fruit samples and annotating the data.YOLOv4 and YOLO v4-Tiny algorithms were used for model training.The precision,recall,mean average precision and comprehensive evaluation index F1 value of the trained models were analyzed.【Results】The precision of cherry tomato fruit recognition prediction was 100%and 96.84%for the trained YOLOv4 and YOLOv4-Tiny models,the recall rates were 91.12%and 90.53%,average precision was 96.32%and 94.18%,and the comprehensive evaluation index F1 values were 0.95 and 0.94,respectively.【Conclusion】YOLOv4 algorithm model was obviously better than YOLOv4-Tiny,could achieve accurate cherry tomato fruit recognition.