DEVELOPMENT OF A TRAFFIC SIGN DETECTION AND RECOGNITION SYSTEM

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CHAPTER ONE

1.1       INTRODUCTION

Increasing traffic at intersections within urban areas caused concern for safety and congestion as early as the 1850s. The first attempt at controlling intersection traffic was the development of manually turned semaphores, operated by police officers, in London, England in 1868. These devices were first introduced in the United States in 1908 in New York, and their use quickly spread. The electrification of urban areas led to the development and installation of the first electrically operated traffic signal in Cleveland, Ohio in 1914. In 1919, New York, began converting from hand-cranked semaphores to electromechanical controllers. In 1923, Garrett Morgan patented the Morgan traffic signal, which was later sold to General Electric. By 1932, the last hand-cranked semaphore on Parkside Avenue in Brooklyn was replaced by an electromechanical controller. (Kell, J.H., and I.J. Fullerton, 1991)

For nearly 50 years, from the 1920s until the 1970s, the electromechanical controller dominated the traffic signal systems market. Cycle lengths were programmed by installing appropriate gears and the cycle was split into various intervals by inserting pins on a timing dial. To accommodate variations in traffic demands, the concept was extended to provide “three dials.” Also, to ensure that adjacent intersections were operating as a “traffic signal system” with predictable cycle lengths, splits, and offsets, a “seven-wire” interconnect procedure was developed so that adjacent electromechanical controllers could work together in a systematic manner. Even as we reach the millennium, some urban areas have traffic signal systems based on three-dial electromechanical controllers and seven-wire interconnect systems. Furthermore, much of the terminology developed to describe the electromechanical systems is still in use today to describe parameters in modern microprocessor-based controllers. (Andrews, C. M, 1997)

Traffic Sign Recognition (TSR) systems square measure designed to acknowledge the road signs like “speed limit” or “do not enter” and real-world atmosphere. Traffic signs square measure put in to guide, warn, and regulate traffic. Within the globe, after they get tired, drivers might not continuously notice traffic signs.  At the hours of darkness time, drivers’ square measure simply plagued by headlights of returning vehicles and will miss road signs.

In implementer’s season, traffic signs square measure more durable to acknowledge quickly and properly.  Among fully completely different causes of accidents, some major causes square measure cognition of the road sign, occlusion of the road sign and distraction of the drivers.   These items would possibly cause traffic accidents and significant injuries. In TSR like Advanced Driver help Systems (ADAS), TSR provides to the motive force the desired data concerning the traffic rules by observance the traffic sign posts.

The most objective of a traffic signs recognition system is to acknowledge one or a lot of road signs from complicated digital pictures returning from video camera, mounted on a vehicle moving on the roads or the highways.   This can be a troublesome task, considering the complexness of out of doors scenes and also the variation of lighting and shadowing conditions. Lighting conditions could be a terribly troblue some drawback to constarin and regulate at the moment, TSR systems square measure comprises of 2 parts:

  1. sign detection
  2. sign classification

 Most of the approaches on sign detection square measure supported colour data. Road and traffic sign recognition is an important field in Intelligent Transport Systems (ITS). This is because of the importance of road signs and traffic signals in daily life. They define a visual language that can be interpreted by drivers. They represent the current traffic situation on the road, show the danger and difficulties around the drivers, give warnings to them, and help them with their navigation by providing useful information that makes the driving safe and convenient. The human visual perception abilities depend on the individual’s physical and mental conditions. In certain circumstances, these abilities can be affected by many factors such as tiredness, and driving tension.

Hence, it is very important to have an automatic road sign recognition system that can be a subsidiary means to the driver  Giving this information in a good time to the drivers can prevent accidents, save lives, increase the driving performance, and reduce the pollution caused by the vehicles.  Road-sign detection and recognition has many advantages and applications in the field of the Intelligent Transport Systems (ITS). Driver Support Systems (DSS) can detect and recognize road-signs in real time. This helps to improve the traffic flow and safety and avoids hazardous driving conditions, like collisions. Traffic sign detection and classification is one of the less studied subjects in the field of Driver Support Systems.

Research groups have focused on other aspects, more related with the development of an automatic pilot, such as the detection of road borders or the recognition of obstacles in the vehicle’s path such as other vehicles or pedestrians.  For example, accidents can occur because drivers don’t notice a sign in the proper time or by lack of attention at a critical moment.  In bad weather conditions such as heavy rain showers, fog, or snow fall, drivers pay less attention to the road signs and concentrate on driving. In night driving, visibility is affected by the headlights of traffic coming from the opposite side and drivers could easily be blinded by this light.

1.2       Statement of the Problem

            Traffic Congestion problem is a phenomenon which contributed huge impact to the transportation system in country. This causes many problems especially when there are many emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. Traffic control establishes a set of rules and instructions that drivers, pilot, train engineers and ship captains rely on to avoid collision and other hazards.

            Motorists depend on traffic control devices to avoid collision and travel safely to their destinations.

            Traffic control devices for highway travel include signs, signal light, pavement marking and a variety of devices placed on over, near, or even under the road way.

1.3  Aim of the Study

The  aims of this project is to present a new road sign detection and recognition system based on using fuzzy segmentation Approach and Artificial neural networks as a Network classifier. The system uses a combination of a sign’s border color and pictogram’s color to detect the sign. Two different Fuzzy Approach and Artificial neural networks.

1.4  Objectives of the Study

The objective of this road sign detection and recognition system based on using fuzzy segmentation Approach and Artificial neural networks as a Network classifier is to include the following:

  1. Describes the potential difficulties of dealing with traffic signs
  2. To recognize and classify the traffic  signs.
  3. To present properties of hue and the light model for shadows and highlights

1.5  Significance of the Study

Road and traffic signs have been designed to be principally distinguishable from the natural and/or man-made backgrounds. They are characterized by many features make them recognizable with respect to the environment. Road signs are designed, manufactured and installed according to tight regulations. They are designed in fixed 2-D shapes like triangles, circles, octagons, or rectangles. The colours of the signs are chosen to be far away from the environment, which make them easily recognizable by the drivers. The information on the sign has one colour and the rest of the sign has another colour. The tint of the paint that covers the sign should correspond to a specific wavelength in the visible spectrum The signs are located in well-defined locations with respect to the road.

1.6 Scope of the Study

This scope of the development of a traffic sign detection and recognition system based on using fuzzy segmentation Approach and Artificial neural networks as a Network classifier. The modern method of traffic control system is indicating each light at a particular time interval to pass a vehicle at one lane and stop vehicle on the other lane. The light is broken into three (3) categories (Red, Yellow, and Green) to signal to lane on what to do at a particular time where the red signify STOP, yellow signify READY, and green signify MOVE. Each light is designed to turn itself on whenever it is necessary and turn off the time elapse.

1.7  Limitation of the Study

The identification of the road signs is achieved by two main stages: detection, and recognition. In the detection phase, the image is pre-processed, enhanced, and segmented according to the sign properties such as colour or shape. The output is a segmented image containing potential regions which could be recognized as possible road signs. The efficiency and speed of the detection are important factors which play a strong role in the whole process, because it reduces the search space and indicate only potential regions.

1.8 Definition of Terms

  1. Traffic Light: A road signal for directing vehicular traffic by means of color lights, typically red for stop, green for go, and yellow for proceed with caution.
  2. Traffic System: refers to information and communication technology  that improve transport outcomes such as transport safety, transport productivity, travel reliability, informed travel choices, social equity, environmental performance and network operation resilience.
  3. Traffic sign detection:  It study subjects in the field of Driver Support Systems
  4. Sign detection : sign detection measure supported colour data.  is an important field in Intelligent Transport Systems (ITS).
  5. Traffic Sign Recognition (TSR) :systems square measure designed to acknowledge the road signs like “speed limit” or “do not enter” and real-world atmosphere.
  6. Traffic signals: in daily life. They define a visual language that can be interpreted by drivers. They represent the current traffic situation on the road, show the danger and difficulties around the drivers, give warnings to them, and help them with their navigation by providing useful information that makes the driving safe and convenient
  7. Driver Support Systems(DSS): can detect and recognize road-signs in real time. This helps to improve the traffic flow and safety and avoids hazardous driving conditions, like collisions. Traffic sign detection and classification is one of the less studied subjects in the field of Driver Support Systems.
  8. Intelligent Transport Support: Intelligent transportation systems (ITS) include both the traffic stream control and intelligent vehicles. Cell phone networks and global positioning systems (GPS) enable the use of geographical information (GI) so that individual vehicles can locate themselves and global transportation systems can be enhanced taking advantages of new information technology solutions and algorithms.

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