Fine-grained visual recognition requiring domain-specific and expert knowledge is a difficult issue in computer vision.This study addresses the problem of fine-grained visual categorization on accuracy and speed.This task is full of challenges because all the objects in a fine-grained dataset belong to the same level category

with some fine

subtle differences between classes.These subtle differences cannot be easily distinguished by ordinary people who do not have expert knowledge.Moreover

the features cannot be easily extracted and classified into the correct category by the computer.This study uses the convolutional neural network to extract features to predict the fine-grained class and thus improve recognition accuracy.The task includes object detection

object recognition

and object classification. In this study

we propose a convolutional neural network architecture based on deep region networks.We use this architecture because the convolutional neural network can improve the extraction and classification accuracy significantly.The deep architecture can also extract fine and coarse features that are useful for classification.First

we extract deep features by use of the feature extraction network to create a feature map.Every convolution layer extracts the feature by feature weight matrix

which is set by forward and back propagation algorithms to minimize the loss function.The proposed feature extraction network possesses two different architectures

i.e.

VGG and Residual networks.We use the VGG 16-layer and Res 101-layer deep networks as the feature extraction networks.We share the deep features extracted by the feature extraction network to the subsequent network.Second

we use the region proposal network architecture to implement the region proposal step.The feature map extracted by the feature extraction network is convoluted with a convolution kernel size of 3×3 with three different feature sizes and aspect ratios.Through convolution of the feature map

we derive 512 dimension vectors that can be used to conduct bounding box classification and regression.We determine the region of interest (RoI) if the class is yes.RoI pooling is used to ensure maximum pooling on the RoI.This pooling shares the network and avoids repeated computation.Third

the proposed regions are inputted into the region convolutional neural network to predict the class and score of every fine-grained classification and compute the regression function of the bounding box of object detection.Finally

the deep region convolutional neural network outputs four results.Two results are the classification results that the network predicted with maximum and minimum scores.The two other results are the regression results that include four values

i.e.

and

and two coordinates

i.e.

the lower left corner (

) and the upper right corner (

)

which form a rectangular area. We use the CUB_200_2011 dataset

which is designed for fine-grained image recognition with public annotations

including class labels

object bounding boxes

and part locations

to conduct fine-grained recognition of birds.The dataset contains 200 fine-grained categories of birds and a total of 11

788 pictures.Only the class label and object bounding box annotations are used in our evaluation.In this work

the proposed networks are trained and tested to achieve superior performance.For the entire bird

the VGG16+R-CNN(RPN) network achieves the Top 1 accuracy of 90.88% and the Res101+R-CNN(RPN) network achieves the Top 1 accuracy of 91.72%.Meanwhile

the VGG16+R-CNN(RPN) network achieves the Top 5 accuracy of 98.15% and the Res101+R-CNN(RPN) network achieves the Top 5 accuracy of 98.24%.The Top 5 accuracy of the two networks is more than 98%.We also conduct experiments on certain parts of the bird

such as the head.For the head of the bird

the VGG16+R-CNN(RPN) network achieves the Top 1 accuracy of 90.70% and the Res101+R-CNN(RPN) network achieves the Top 1 accuracy of 91.06%.Meanwhile

the VGG16+R-CNN(RPN) network achieves the Top 5 accuracy of 98.04% and the Res101+R-CNN(RPN) network achieves the Top 5 accuracy of 98.07%.In this study

we analyze the effect of certain parts of the bird

such as beak

head

belly

leg

and foot

on the performance of object detection

object recognition

and object classification. This study proposes a network architecture based on deep region convolutional neural networks that achieve superior performance compared with other models in terms of object detection

object recognition

and object classification

although fine-grained visual recognition is difficult for ordinary people and other network architectures.Our method exhibits a high accuracy and a good performance and does not need extra data to train

which make it robust and applicable to various datasets.Experimental results show that the local information is useful for fine-grained image recognition.The proposed model is applicable to object detection and fine-grained image categorization.The experimental results also show that the local information

such as head

is useful for fine-grained image recognition.