关键词: 杂草识别, Faster R-CNN, 迁移学习, 双阈值非极大抑制算法

Abstract: Weed is an important factor leading to crop yield loss. Aiming at the problems of low accuracy and small range of weed identification in the natural environment of the field, an identification algorithm of field weeds based on improved Faster R-CNN and transfer learning is proposed. Firstly, weed images of different periods and angles in multiple scenes were collected, and the data set expended by rotation, clipping, color adjustment, etc. Then, based on the original Faster R-CNN, the improved dualthreshold nonmaximum suppression algorithm was used to find the bounding boxes with high confidence. Finally, AlexNet, GoogleNet, VGG16 and ResNet50 were used as the region proposal network of the model, and the optimal model parameters were transferred to the filed weed identification task. Through testing and verification on multispecies dataset and small sample dataset, the experimental results showed that our method could achieve precision of 9658%, recall of 94.82% and F1-score of 95.06%, and it has a wider identification range than current mainstream methods while maintaining a higher identification accuracy.

Key words: weeds identification, Faster R-CNN, transferring learning, dualthreshold nonmaximum suppression algorithm