Research Article Open Access

RF-CNNS: Thin Deep Learning Networks For Accelerating Traffic Signs Recognition

Btissam Bousarhane1 and Driss Bouzidi1
  • 1 Smart Systems Laboratory (SSL), National School of Computer Science and Systems Analysis ENSIAS, Mohammed V University in Rabat, Morocco

Abstract

Road scene analysis is a wide domain of research that aims to ameliorate the environmental perception in intelligent transportation systems, including autonomous vehicles and advanced driver-assistance systems. It also plays a crucial role in road safety improvement, by contributing to the reduction of traffic accidents' rate. Ensuring an efficient recognition of traffic signs contributes enormously to making cars safer, which in consequence helps to save more lives on roads. To ensure this recognition, many approaches are adopted, especially deep learning ones and more specifically convolutional neural networks. In effect, these networks have proven their high performances in many fields of computer vision research, including traffic sign recognition. However, although their high performances, many limitations still face their implementation, especially in real-time applications and resource-constrained environments. From this perspective, creating a certain balance between the model complexity and the classification accuracy, through a computationally efficient network, is the main objective of this study. To achieve this goal, a receptive fields architecture is adopted to preserve and optimize the connectivity between the different units of the network. Based on this architecture, two receptive field networks are proposed, with reduced complexity and enhanced generalizability. Using two public datasets, the obtained results show that the adopted approach ensures high classification accuracies and considerably accelerates the inference stage. The obtained accuracy is about 98.49%, using the Belgium traffic signs classification dataset, while the inference time is less than 500 us per image. Jones & Samson. T. rossicum was characterized by a higher level of inhibitory activity. It was found that with simultaneous seeding of Trichoderma and test objects on Petri dishes, rapid growth of T. rossicum was observed in all variants of the experiment, except for the variant with Aspergillus niger Tiegh, where suppression of Trichoderma sporulation was also observed.

Journal of Computer Science
Volume 19 No. 10, 2023, 1263-1282

DOI: https://doi.org/10.3844/jcssp.2023.1263.1282

Submitted On: 18 March 2023 Published On: 18 September 2023

How to Cite: Bousarhane, B. & Bouzidi, D. (2023). RF-CNNS: Thin Deep Learning Networks For Accelerating Traffic Signs Recognition. Journal of Computer Science, 19(10), 1263-1282. https://doi.org/10.3844/jcssp.2023.1263.1282

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Keywords

  • Traffic Signs Classification
  • Receptive Fields Feedforward Network
  • Receptive Fields CNN
  • Locally Connected Network