Development of Smart Traffic Light Controller System with Deep Learning Capability in Image Processing

Siu Hong Loh, Jia Jia Sim, Chu Shen Ong, Kim Ho Yeap, Peh Chiong Teh, Kim Hoe Tshai


The traffic congestion at the junction is becoming one of the major issues for many cities all around the world. One of the reasons causing this issue is due to the inefficient of the existing traffic light system at the traffic junction. This paper proposes a Smart Traffic Light Controller System (STLCS) with deep learning capability in image processing. The developed STLCS is comprised of Altera DE2 board, personal computer and Intel Neural Compute Stick 2 (NCS2). The personal computer is used as the vehicle detection system of the STLCS by performing various computer vision tasks and inference. The tasks include image acquisition, processing, and vehicle detection and counting. The smart feature of the system can detect the vehicles by using deep learning model and compute a flexible green time for each lane according to the density of traffic in each lane. The vehicle detection emphasizes the image processing by using the deep learning algorithm from the pre-trained model to increase the efficiency and computing time of the system. The efficiency of the vehicle detection system is about 94.73%


Deep learning; Image processing; Inference engine; Traffic congestion; Vehicle detection.

Article Metrics

Abstract view : 27 times
PDF - 13 times

Full Text:



Malaysian Automotive Association, Sales & Production Statistics., 2021 (accessed 27.01.2021).

T. E. Somefun, C. O. A. Awosope, A. Abdulkareem, E. Okpon, A. S. Alayande and C. T. Somefun, Design and implementation of density-based traffic management system, International Journal of Engineering Research and Technology, 13, 2020, 2157-2164.

P. Sinhmar, Intelligent traffic light and density control using IR sensors and microcontroller, International Journal of Advanced Technology & Engineering Research, 2, 2012, 30-35.

P. Choudekar, S. Banerjee and M. K. Muju, Implementation of image processing in real time traffic light control, 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India, 2011.

Kushi, Smart control of traffic light system using image processing, 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India, 2017.

A. H. M. Almawgani, Design of real time smart traffic light control system, Proceedings of ISER 108th International Conference, Mecca, Saudi Arabia, 2018, 51-55.

A. Osarenomase, F. S. Bala and G. Bakare, Field programmable gate array based intelligent traffic light system, International Journal of Engineering and Innovative Technology, 4, 2015, 10-16.

B. Dilip, Y. Alekhya and P. D. Bharathi, FPGA implementation of an advanced traffic light controller using Verilog HDL, International Journal of Advanced Research in Computer Engineering & Technology, 1, 2012, 1-6.

S. U. Holambe and D. B. Andore, Advance traffic light control system based on FPGA, International Journal of Scientific Engineering and Research, 3, 2015, 31-35.

D. Bhavana, D. R. Tej, P. Jain, G. Mounika and R. Mohini, Traffic light controller using FPGA, International Journal of Engineering Research and Applications, 5, 2015, 165-168.

V. S. Rakesh and V. Shaithya, A traffic control system using inductive loop detector, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 4, 2015, 4590-4593.

R. A. Asmara, B. Syahputro, D. supriyanto and A. N. Handayani, Prediction of traffic density using YOLO object detection and implemented in Raspberry Pi 3b + and Intel NCS 2, 2020 IEEE 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, 2020.

T. Tsai, C. Hsu and W. Wan, Live demonstration: Vision-based real-time fall detection system on embedded system, 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, Spain, 2020.

E. Z. Q. Koh, A. Y. Abdalla and H. Nugroho, Visual computing-based perception system for small autonomous vehicles: Development on a lighter computing platform, 2020 IEEE Student Conference on Research and Development (SCOReD), Johor, Malaysia, 2020.

L. A. Elefteriadou, Highway Capacity Manual, 6th Edition: A Guide for Multimodal Mobility Analysis. Washington, DC: The National Academies Press, 2016.


  • There are currently no refbacks.