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Cross-Granularity Network for Vehicle Make and Model Recognition
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Vehicle Make and Model Recognition (VMMR) is a fine-grained classification task in Intelligent Transportation System (ITS). Recent works address VMMR through feature encoding schemes, part-based methods or attention modules. Despite their astounding results, these techniques concentrate on the high-level semantic features. This practice cripples the feature expressive ability of the networks as the granular traits of the vehicle distilled from the early convolution layers are not embedded into the final feature representations. In this work, by contrast, a Cross-Granularity (CG) module which is responsible for the integration of macroscopic and microscopic components is proposed. By incorporating the CG module into a Convolutional Neural Network (CNN), the resultant network i.e. CGNet reinforces the feature extraction ability by amalgamating the feature maps from different scales to render a balanced mix between local contextual information and global semantic details. To validate the proposed framework, experiments are conducted on four publicly available datasets. We report competitive performance on web-nature Comprehensive Cars, Stanford Cars, Car-FG3K and surveillance-nature Comprehensive Cars datasets with 98.3%, 95.4% 86.4% and 99.1% accuracies. Furthermore, we demonstrate the ability of the CGNet to pinpoint distinctive fine-grained details via the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique and compare it against the baseline which learns on deep features alone. The generalization ability of the CG module on other CNNs is also examined and the results suggest a high compatibility between the two.
Shi Hao Tan、Joon Huang Chuah、Chee-Onn Chow、Jeevan Kanesan
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Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia|Faculty of Engineering and Information Technology, Southern University College, Skudai, Malaysia
NETL
NSTL
IEEE
