FF-CMnet: A CNN-Based Model for Fine-Grained Classification of Car Models Based on Feature Fusion

摘要

We present in this paper a novel scheme for fine-grained car model classification based on convolutional neural network and feature fusion. This scheme is called FF-CMNET (Feature Fusion based Car Model Classification Net) and is based on the principle that the car frontal images can be partitioned into upper and lower parts that exhibit distinct feature distributions but are still structurally correlated to allow feature fusion. The characteristics of FF-CMNET include: (1) the design of two separate branches, named UpNet and DownNet, for extracting the features of upper parts and lower parts of the car frontal images separately; (2) a two-step fusion of features at the output of UpNet and DownNet and then again in FusionNet; and (3) the adoption of small convolution kernels and global average pooling. Extensive experiments conducted on a benchmark dataset, CompCars, show favorable results which demonstrate that the proposed FF-CMNET is able to outperform the state-of-the-art models in the classification of large datasets.