Research And Implementation Of Fine-grained Image Recognition Model Based On Bilinear CNN

The continuous improvement of modern industry and science and technology promotes the development of social economy.However,the resulting industrial pollution has brought severe challenges to the construction of human ecological civilization and the protection of species diversity,and the living environment of many wild animals has been polluted and destroyed.China is one of the countries with the largest number of birds in the world,with more than 1400 species of wild birds.Therefore,the monitoring,identification and protection of wild birds are particularly important.Traditional bird identification is usually carried out by manual monitoring and by the help of relevant professionals one by one.It is difficult to combine efficiency and accuracy.With the rapid development of modern artificial intelligence technology,it has become a new research hotspot for people to collect images of wild birds by aerial photography equipment such as unmanned aerial vehicles,and then use fine-grained image recognition algorithm for unmanned monitoring and recognition.This fine-grained image recognition method can not only save time and labor costs,but also bring good social benefits and promote the development of science,which has very important research value and significance.Fine-grained image recognition is different from the general image recognition research task,which aims to subdivide objects of different subclasses under the same category more finely.Common fine-grained image recognition research objects include a variety of objects such as birds,dogs,cars,airplanes,flowers,plants,etc.,represented by bird identification.Bird identification can best reflect the research characteristics and difficulties of fine-grained image recognition,so it has been widely concerned and involved by researchers at home and abroad.This paper focuses on the research of fine-grained image recognition methods for wild birds.Firstly,the research status and research methods of fine-grained image recognition are comprehensively analyzed.Bilinear CNN,a representative fine-grained image recognition model,is used for bird identification research,and some problems in the feature learning phase of the bilinear CNN model are analyzed,and further model improvement strategies and methods are proposed.This paper introduces a method of deep residual learning structure and visual attention mechanism,and proposes an improved bilinear model BRAN that integrates a binary residual attention module,using the powerful feature extraction capability of deep residual network and multi-dimensional attention feature fusion method effectively improves the effect of the original model on the fine-grained image recognition problem.The classification accuracy of the model on the fine-grained image benchmark dataset CUB-200-2011 reached 87.2%,which not only improved the recognition effect of the original bilinear CNN model on the dataset,but also exceeded the classification accuracy of some mainstream fine-grained image recognition algorithms.Finally,this paper also encapsulates the improved model,designs and implements a fine-grained bird image recognition system via Web platform.