Enhancing Vehicle Make and Model Recognition with 3D Attention Modules

TL;DR

This paper introduces a 3D attention module integrated into CNNs to improve vehicle make and model recognition by focusing on critical features, achieving state-of-the-art accuracy without increasing model parameters.

Contribution

The study proposes a novel 3D attention module for CNNs that enhances fine-grained vehicle recognition without adding extra parameters.

Findings

Achieved 90.69% accuracy on Stanford Cars dataset.

Outperformed existing CNN and transformer models.

Effective focus on distinguishing vehicle features.

Abstract

Vehicle make and model recognition (VMMR) is a crucial component of the Intelligent Transport System, garnering significant attention in recent years. VMMR has been widely utilized for detecting suspicious vehicles, monitoring urban traffic, and autonomous driving systems. The complexity of VMMR arises from the subtle visual distinctions among vehicle models and the wide variety of classes produced by manufacturers. Convolutional Neural Networks (CNNs), a prominent type of deep learning model, have been extensively employed in various computer vision tasks, including VMMR, yielding remarkable results. As VMMR is a fine-grained classification problem, it primarily faces inter-class similarity and intra-class variation challenges. In this study, we implement an attention module to address these challenges and enhance the model's focus on critical areas containing distinguishing features. This module, which does not increase the parameters of the original model, generates three-dimensional (3-D) attention weights to refine the feature map. Our proposed model integrates the attention module into two different locations within the middle section of a convolutional model, where the feature maps from these sections offer sufficient information about the input frames without being overly detailed or overly coarse. The performance of our proposed model, along with state-of-the-art (SOTA) convolutional and transformer-based models, was evaluated using the Stanford Cars dataset. Our proposed model achieved the highest accuracy, 90.69\%, among the compared models.