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

作　　者：Yahia Said Yahya Alassaf Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第2期3005-3018,共14页计算机、材料和连续体(英文)

基　　金：funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).

摘　　要：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.

关 键 词：Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] U495[自动化与计算机技术—控制科学与工程]