Few-shot potato disease leaf detection based on hierarchical feature alignment network
In order to address the problems of the over-reliance on large amounts of training data and the poor generalization of unseen disease identification in traditional potato disease leaf detection methods,a few-shot potato disease leaf detection model based on hierarchical feature alignment network is proposed.Firstly,a weakly labeled dataset containing various types of potato diseases were collected and annotated.Secondly,the multi-modal bi-modal feature semantic representations of textual and visual semantics in the support branch were established,and multiple candidate boxes were generated using a pre-trained region proposal network.Thirdly,a convolutional neural network was adopted to map the candidate box regions into deep feature space,and feature alignment was performed between textual and visual semantics using an unparameterized metric method.Finally,the similarity was computed between the unseen class disease images in the query branch and the multi-modal visual and textual semantic association set,and the disease category of the unseen new class was quickly provided according to the similarity value.Through testing on self-built potato disease leaf datasets and open source datasets,the proposed models can achieve recognition accuracy of 93.55％and 96.35％on the test sets,respectively,and 95.15％and 94.06％on the cross-domain datasets,which is superior to the current classical object detection models.The proposed method has certain practical application value.
万方数据
维普
