Open Access

ARTICLE

Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

Yahia Said^1,2,*, Yahya Alassaf³, Refka Ghodhbani⁴, Taoufik Saidani⁴, Olfa Ben Rhaiem⁵

1 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, 91431, Saudi Arabia
2 Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia
3 Department of Civil Engineering, College of Engineering, Northern Border University, Arar, 91431, Saudi Arabia
4 Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia
5 College of Science, Northern Border University, Arar, 91431, Saudi Arabia

* Corresponding Author: Yahia Said. Email:

(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)

Computers, Materials & Continua 2025, 82(2), 3005-3018. https://doi.org/10.32604/cmc.2025.060928

Received 12 November 2024; Accepted 17 December 2024; Issue published 17 February 2025

Abstract

Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.

---

Keywords

---