Image Classification of Biota Specimens Based On Multi-Scale Bilinear Feature Fusion Model

Abstract

Objective Biological image detection and classification is a classic and challenging task at present. In order to accurately and automatically identify biological species and ownership relationship, a new classification model and tree structure are proposed to improve the performance of biological detection or classification. Methods Two groups of experiments were set up. Using YOLOv5 as the first layer of the tree structure, YOLOv5 is easy to deploy and can roughly locate the image and remove noise information beyond the target. Then the first group of experiments were trained with RESNET-101, RESNET-200 and DenSenet-201 at different levels of biological tree structure, respectively, and the results were compared with those directly using YOLOv5. In the second group of experiments, the new multi-scale bilinear feature fusion classification model is used to classify images successively, and finally obtain the accurate classification of objects. Result In the first group of experiments, the best result of the customized data set of 80 insect species was 1.09% higher than the mAP value of YOLOv5x. In the second group of experiments, the accuracy of cuB-200-2011(Caltech- UCSDbirds-200-2011) data was 86.4%. The accuracy is 2.3% higher than that of B-CNN classification model, which verifies the effectiveness of the improved method and model in this paper. Conclusion More complementary information can be obtained by multi-scale feature fusion. Bilinear fusion of features at different scales can fully express features at different scales and improve the accuracy of classification tasks. The tree structure effectively removes irrelevant noise information, and at the same time classifies layer by layer, simplifies the classification task, enriches the feature information, and makes the result more accurate