关键词: 灵武长枣, Faster R-CNN, L-softmax, A-softmax, 平均精度

Abstract:

When machine vision technology is used to automatically pick local characteristic forest fruit Lingwu long jujube, the naturally changing environment will greatly affect the accuracy of detection. In order to adapt to the changing natural environment, an area convolutional neural network（Faster R-CNN） ensemble learning model based on double loss function is proposed. Firstly, an image data set is established, including a training set and a test set. Secondly, a Faster R-CNN model is built based on the features. The RPN layer uses softmax as the basic classifier to obtain the region of interest. This feature map is combined with a layer of loss function. Use Large Marge Softmax Loss（L-softmax） and Angular Softmax Loss（A-softmax） to do loss calculations, and take various maximum values. Finally, load the training set for image training to get the detector, and use the trained detector. The result image is obtained from the test set, and the image detection is completed. Compared with the single loss function network, the standard ResNet101 and ResNet50 network structures, the image detection network method in this paper：the accuracy rate is 0.9826, the recall rate is 0.9213, and the average accuracy is 0.9.

Key words: Lingwu long jujube, Faster R-CNN, L-softmax, A-softmax, average precision