{"id":3402,"date":"2022-04-03T14:45:08","date_gmt":"2022-04-03T14:45:08","guid":{"rendered":"https:\/\/www.hnjournal.net\/?page_id=3402"},"modified":"2022-04-03T14:45:13","modified_gmt":"2022-04-03T14:45:13","slug":"3-4-12","status":"publish","type":"page","link":"https:\/\/www.hnjournal.net\/ar\/3-4-12\/","title":{"rendered":"Design Intelligent Traffic Light System Using Image Processing"},"content":{"rendered":"

Assist.Prof. Dr. PER\u0130 G\u00dcNE\u015e1<\/sup><\/strong> OSAMA BITTAR2<\/sup><\/strong><\/p>\n

Instructure at \u0130stanbul Aydin University<\/p>\n

Email: parvanehshams@aydin.edu.tr<\/a><\/p>\n

2<\/sup> Master’s Student in Computer Science at Istanbul Ayd\u0131n \u00dcniversitesi<\/p>\n

Email:<\/p>\n

Download
\n<\/a><\/p>\n

HNSJ, 2022, 3(4); https:\/\/doi.org\/10.53796\/hnsj3412<\/p>\n

Published at 01\/04\/2022 Accepted at 19\/03\/2022 <\/strong><\/p>\n

Abstract <\/strong><\/p>\n

The rapid development of technology, economy and population growth cause more transportation vehicles to be used in today’s cities. Using more vehicles causes long queues at traffic lights, unnecessary waste of time, unnecessary fuel consumption while waiting at traffic lights and nature pollution.<\/p>\n

In this study, it is to create an intelligent traffic light based on image processing technology, using Python, YOLO v3 and OpenCV Library, where the traffic light knows the number of vehicles available in addition to the number of pedestrians, depending on these numbers the timers of traffic light will be changable and this reduces the unnecessary waiting time. Also, if there is an ambulance, the traffic light will open the way for the cars and help the ambulance to pass quickly, thus helping to save people’s lives. At the end of the search, the proposed traffic light has been compared with a classic traffic light in Istanbul. and the results have proven the superiority of the smart traffic light over the traditional one at many points.<\/p>\n

Key Words: <\/strong>Python, YOLO v3, OpenCV, Object Detection, Traffic Light.<\/p>\n

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1. Introduction<\/strong><\/p>\n

As is known, the biggest cause of traffic congestion in cities is traffic lights where the lighting times of traffic lights cannot adapt to the changing traffic density and cause long and unnecessary queues. Intelligent traffic light control systems have begun to spread with the development of technology in the past two decades, these systems attempt to reduce waiting times at traffic lights and apply pre-determined minimum or maximum lighting times in proportion to the number of pedestrians or vehicles at traffic lights in an attempt to prevent traffic congestion, But finding a fixed time commensurate with the traffic congestion at the traffic light is very difficult because the number of vehicles and pedestrians changes irregularly, and this makes the process of adjusting the time of the traffic light difficult and inaccurate. In addition to what was previously mentioned, there is a problem when an ambulance is in the waiting queue, where traditional traffic lights cannot take any decision, and this will delay the ambulance a lot, which may endanger the lives of patients.<\/p>\n

The proposed solution in this study is to create a smart traffic light based on image processing technology, where the traffic light can count vehicles in addition to counting pedestrians at every moment and thus change the time of the traffic light in proportion to the amount of current congestion and this reduces the unnecessary waiting time, and also in the case Having an ambulance, the traffic light will open the way for vehicles, helping the ambulance pass quickly and reach patients faster.<\/p>\n

2.Literature Review<\/strong><\/p>\n

E. Leigh (2017) designed an intelligent traffic management system, and this system was partially implemented in the city of Cambridge, where the system relies on placing sensors that detect queues for vehicles and then sends the information to a central control unit to take the appropriate decision, but the problems In this way, the networks suffer from many problems, which leads to the slowdown of the central system [1].<\/p>\n

The researcher Kartikeya Jain in (2017) designed a system capable of identifying cars through the number plate, and he used cameras to identify cars. But this system failed in Pakistan because donkey carts and bicycles do not have a number plate and therefore cannot be recognized [2].<\/p>\n

V.S.A.M.S.D.K.K. Swathi (2016) has designed an intelligent system to direct car traffic to the least crowded road. It relies on a set of infrared sensors that can detect traffic density by observing the reflected light from cars. This system suffers from a problem, which is the value of reading the sensors is affected by a degree Heat and humidity make the results of this system inaccurate[3].<\/p>\n

Syed (2009) found that controllers based on fuzzy rules are able to efficiently manage the traffic light system and have optimal decision-making ability. Whereas, traditional constant-time-based control methods cannot effectively deal with a complex and changing traffic situation [4].<\/p>\n

Z.Yang, L.S.C. Pun-Cheng (2018) Image processing systems can detect the complex, where the algorithm extracts the number of vehicles from the video and thus knows the traffic density. Traffic light control systems that rely on image processing are characterized by their ability to pre-process and make the appropriate decision. Image processing techniques have been applied on a very wide scale in Traffic field in order to collect information from the beams in real time[5].<\/p>\n

N.K. Jain, R.K. Saini, and P. Mittal (2019), Estimating traffic density is fundamental to addressing traffic congestion and improving traffic management systems. This requires detection of moving vehicles and tracking of vehicles. Intelligent Transportation Systems (ITS) based on magnetic ring, infrared radar, RADAR microwave, and sensors based on video and ultrasound processing have been used to manage traffic [6].<\/p>\n

Roxanne Hawi and others (2017), an increase in traffic congestion and insufficient space and funding prompted researchers to work towards reducing it. One of these solutions is the use of a smart system such as the use of Smart Traffic Signal (STL) and Wireless Sensor Network (WSN) [7].<\/p>\n

Nathanie Fairfield and Chris Urmson (2011), over the past years the traffic light control system has been static and ineffective. In order to better control the traffic there must be a more efficient and dynamic system that can handle the traffic easily and safely and therefore this system will be especially better in the performance of traffic control Traffic intersections [8].<\/p>\n

T. Kanungo and other(2002), Although the video-based traffic monitoring recognition technology has the characteristics of multiple information gathering, ease of installation and low cost, it is difficult to obtain the required traffic information from the image. In recent years, how to obtain traffic information efficiently, accurately and in real time has become an essential technology for video surveillance [9].<\/p>\n

In this paper, we propose a method capable of determining the number of vehicles and number of pedestrians in real time as well as the ability to distinguish ambulances and make the appropriate decision effectively.<\/p>\n

3.Problem Statement<\/strong><\/p>\n

Traditional traffic lights suffer from the problem of fixed time for vehicles and pedestrians, therefore it is unable to adapt to the requirements of congestion, and this is what causes queues of vehicles and pedestrians. In this study we try to solve this problem by designing a intelligent traffic light that depending on image processing technology and is capable of making real-time decision commensurate with the traffic density.<\/p>\n

4.Data Set<\/strong><\/p>\n

In order to discover the elements in the images, it is necessary to have the appropriate set of images, as the number of images greatly affects the learning algorithm and the greater the number of images, the greater the ability to detect objects in the image.For this reason, several visual collection methods were used in this study.<\/p>\n

4.1.Google fatkun bulk data downloader<\/strong><\/p>\n

The keywords covering all types of vehicles were written in Arabic, English and Turkish in the Google search engine, and then the Fatkun Stack file plugin tool integrated with the Google Chrome browser was used, which helps to download all the image at once instead of downloading the images one by one.<\/p>\n

<\/p>\n

Figure (1): Google fatkun bulk<\/p>\n

4.2.Download Image From Shuttterstock.com<\/strong><\/p>\n

Images containing only ambulance have been downloaded from Shuttterstock.com and istockphoto sites. This was done in order to focus only on objects at the training stage. Downloaded images are in high resolution. It is aimed to increase the accuracy of the neural network model by obtaining details with high resolution images.<\/p>\n

<\/p>\n

Figure (2): Shuttterstock.com website<\/p>\n

4.3.Tagging The Created Dataset<\/strong><\/p>\n

LabelImg program was used to determine the coordinates of the photos taken by labeling them. After the program was opened, the \u201cOpenDir\u201d button was clicked, the file containing all the images was selected and all the images were added to the program.<\/p>\n

<\/p>\n

Figure (3): labeling image using LabelImg program<\/p>\n

4.4.Training YOLO v3 Using Google Colab<\/strong><\/p>\n

After the previous operations an account was created to train YOLO v3 in Google Colab. The folder containing both images and text files created after tagging was uploaded to Google Drive.<\/p>\n

5.Methods<\/strong><\/p>\n

The study was applied to a one-way traffic light as shown in figure (4), where A and B represent the areas pedestrian:<\/p>\n

<\/p>\n

Figure (4): One Way Traffic Light<\/p>\n

The classic traffic light is based on a fixed life cycle, as shown in the figure (5):<\/p>\n

<\/p>\n

Figure (5): Classic traffic light life cycle<\/p>\n

In this study, the work was divided into five steps, image processing technology which depending on YOLO v3 and OpenCV was used in order to detect objects in the images and obtain their number. The work stages are as follows:<\/p>\n