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Dropout-VGG Based Convolutional Neural Network for Traffic Sign Categorization

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Congress on Intelligent Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 114))

Abstract

In the modern era of motor vehicles where number of cars running on road are increasing exponentially, the safety of the people driving or walking along the road is being endangered. Traffic signs plays the most important role in ensuring their safety. The signs provide the necessary warning and information to help the driver to drive in order and prevent any potential danger. With the rise in modern technology, the concept of Self-Driving cars is the new hot topic. To ensure the feasibility of such vehicles, the concept of autonomous traffic sign detection and classification needs to be implemented with maximum efficiency and accuracy in real-time. Thus, from the past few years, researchers have shown keen interest in solving as well as optimizing traffic sign classification problem. Numerous approaches were intended in the past to deal with this problem, yet there is still an immense scope of performance optimization to meet the needs in real-time scenarios. Among all solutions proposed, convolutional neural networks (CNN) have emerged to be the most successful approach to classify traffic signs. In this paper, we have proposed a novel CNN model termed as dVGG. This technique is inspired by the Visual Geometry Group-16 (VGG-16) architecture. VGG-16 is based on dropout regularization approach. Moreover, other data processing techniques like shuffling, normalization and gray scaling are applied, resulting in a more consistent dataset which led to faster model generalization. “dVGG” is able to perform better than the VGG-16 model which was implemented through Transfer Learning. We have applied the proposed model on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The model proposed have gave an average accuracy of 98.44% on the GTRSB dataset.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Panjab University, Chandigarh, India

    Inderpreet Singh, Sunil Kr. Singh, Sudhakar Kumar & Kriti Aggarwal

Authors
  1. Inderpreet Singh
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  2. Sunil Kr. Singh
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  3. Sudhakar Kumar
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  4. Kriti Aggarwal
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Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Singh, I., Singh, S.K., Kumar, S., Aggarwal, K. (2022). Dropout-VGG Based Convolutional Neural Network for Traffic Sign Categorization. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_18

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