Abstract

Traffic sign detection and recognition (TSDR) is a critical feature for both self-driving cars and autonomous vehicles (AVs), as well as for modern advanced driver assistance systems (ADAS). Real-time and accurate traffic sign detection enhances safe driving capabilities of AVs and contributes to optimized traffic flow. In the context of Indian roads, challenges such as diverse sign designs, occlusion, and inconsistent lighting conditions reduce the effectiveness of standard models. Convolutional neural networks (CNNs) form the backbone of many deep learning approaches for object detection and image classification. Various CNN-based techniques have shown significant success in TSDR. However, in real-world traffic environments, deteriorate due to weather, sustain damage, or become partially obscured by rust or dust. To address these challenges, a custom dataset comprising 3,337 images of Indian traffic signs was created. Data augmentation techniques such as blurring, occlusion (cut), and grayscale conversion were employed to enhance dataset robustness. A novel hybrid deep learning framework, YOLOv5-CBAM-EfficientNet, is proposed by integrating you only look once (YOLOv5) as the backbone, EfficientNet, and the convolutional block attention module (CBAM) to improve spatial and channel-wise feature attention. The model achieved a mean average precision (mAP) of 99% while maintaining real-time performance at 45 frames per second (FPS), demonstrating its potential for use in AVs. The results indicate that the proposed model significantly enhances the perception module of AVs operating in the complex Indian traffic environment.

Keywords

Traffic sign detection, Autonomous vehicles, YOLOv5, EfficientNet, Convolutional block attention module (CBAM).

Cite this article

Juyal A, Bhadula S, Sharma S. YOLOv5-CBAM-EfficientNet: an optimized deep learning framework for traffic sign recognition in autonomous driving . International Journal of Advanced Technology and Engineering Exploration. 2025; 12(130):1432-1448

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