基于YOLOv3深度卷积神经网络的田间百香果定位

    Passion Fruit Localization in the Field Based on YOLOv3 Deep Convolutional Neural Network

    • 摘要: 为实现大田棚架栽培环境下百香果的机器自动化采摘,使用YOLOv3深度卷积神经网络建立了复杂背景下的百香果果实定位模型。该方法使用单个卷积神经网络遍历整个图像,回归目标的类别和位置,实现了直接端到端的目标检测。训练集使用了400张人工标注的图像,测试集为100张图片,共包含3 071个百香果样本。经过训练的模型在测试集下的平均精度均值mAP为97.66%,当使用0.65置信阈值时,准确率为98%,召回率为94%,交并比IOU为83.96%。

       

      Abstract: In order to realize automatic picking of passion fruit under the environment of field shelving, YOLOv3 deep convolutional neural network was used to establish the fruit location model of passion fruit under the complex background. In this method, a single convolutional neural network was used to traverse the whole image and return to the category and location of the target thus to realize the direct end-to-end target detection. The training set used 400 manually labeled images and the test set used 100 images, which included a total of 3,071 passion fruit samples. The average accuracy mean mAP of the trained model under the test set was 97.66%. When the confidence threshold of 0.65 was used, the accuracy rate was 98%, the recall rate was 94%, and the crossover ratio of IOU was 83.96%.

       

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