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S. Rithik Reddy, V. Siddartha, P. Vaishnavi
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Page No: 1 - 14
Abstract : Traffic sign detection is a crucial task for autonomous vehicles, enabling safe navigation through complex environments. Traditional Convolutional Neural Networks (CNNs) are highly effective in detecting traffic signs, but their computational complexity and large memory requirements make them challenging to deploy in real-time systems, especially on resource-limited devices. Binary Neural Networks (BNNs) provide a more efficient alternative by simplifying computations and reducing memory usage, enabling faster processing, but they often sacrifice the detailed features that CNNs capture. Group Convolutional Neural Networks (GCNNs), on the other hand, excel at understanding spatial relationships, such as how traffic signs relate to their surrounding environment and road networks. By combining the computational efficiency of BNNs with the spatial awareness of G-CNNs, this approach aims to deliver a solution that balances speed, efficiency, and the ability to detect traffic signs with contextual understanding.
Keyword Convolutional Neural Networks (CNNs), Binary Neural Networks (BNNs), Group Convolutional Neural Networks (G-CNNs), Image recognition.
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Abstract

Traffic sign detection is essential for autonomous vehicles to navigate roads safely and accurately. While Convolutional Neural Networks (CNNs) are effective in identifying traffic signs, their high memory and processing requirements make them difficult to deploy on low-power devices. Binary Neural Networks (BNNs) offer a more efficient alternative by reducing computational load and memory usage, allowing faster performance. However, this often comes at the cost of reduced feature detail.

Group Convolutional Neural Networks (G-CNNs) excel at capturing spatial relationships, such as the position of signs within a broader road environment. By combining the speed and efficiency of BNNs with the contextual awareness of G-CNNs, this approach aims to create a balanced solution. It supports real-time traffic sign detection while preserving essential spatial features, making it suitable for autonomous driving applications.

Introduction

Accurate traffic sign recognition is critical for autonomous vehicles to operate safely in real-world environments. However, the task is complicated by varying light conditions, small object sizes, and occlusions in urban settings. Traditional methods, which rely on detecting colors and shapes, often struggle under such complex circumstances.

Deep learning, especially using Convolutional Neural Networks (CNNs), has improved traffic sign recognition significantly. CNNs extract rich visual features and enable more reliable detection. For instance, the multi-scale fusion model SQ-RCNN enhances small sign detection by combining features from different layers. This results in higher accuracy and reduced training time, supporting real-time performance.

Binary Neural Networks (BNNs) provide another efficient solution by binarizing weights and activations. This reduces memory usage and speeds up inference, making them ideal for systems with limited hardware capacity. Yet, BNNs may lose some fine-grained details in the process.

To overcome these trade-offs, our research integrates the benefits of CNNs, BNNs, and Group Convolutional Neural Networks (G-CNNs). While CNNs focus on feature extraction, BNNs ensure efficiency, and G-CNNs improve spatial understanding. This hybrid approach enhances detection speed, accuracy, and contextual analysis, aligning with the demands of real-time autonomous driving.

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