A fine-grained image classification method combining YOLOv7 and bilinear multi-level feature fusion
Min Huang, Zehua Wang, Saixing Zhu · 2022
Fine grain image is an important research field of image recognition, which can classify objects in more detail. Image feature extraction is usually implemented by bilinear network, which can meet the desired effect of feature extraction, but also discard some detailed feature information. In this study, a fine-grained classification optimization algorithm that combines the target detection method YOLOv7 and multi-feature fusion bilinear network is proposed to achieve fine-grained image classification. In the method, YOLOv7 is used to locate the target, and the recognition content is extracted according to the image detection frame; Then, the improved bilinear convolutional neural network structure is used to fuse the features of different channels and different levels of convolutional layers in the bilinear network, so as to obtain more feature information and improve the precision of fine-grained classification. Experimental results show that the classification results of this algorithm are better.