Implementation of Road Sign Detection using Arduino

Abstract: Road sign detection is very important for a smart transportation system. Proper road sign detection enhances the security, safety of driver which is required for current situations of the road, such as notices, bans, limitations, and other valuable driving information. The color detection from road sign technique used for verification and classification of the road signs. Our paper introduces a traffic sign detection and recognition system that accurately estimates the situation and exact boundary of traffic signs using Arduino UNO. Our proposed work is ready to read and understand traffic signs which are a really important task for all autonomous vehicles. Keywords: Arduino Uno, Traffic signals, Road safety, Speed control 1. Introduction In today's world, almost everything we do has been simplified by automated tasks. In an attempt to focus on the road while driving, drivers often miss out on signs on the side of the road, which could be dangerous for them and for the people around them. This problem can be avoided if there was an efficient way to notify the driver without having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) play an important role here by detecting and recognizing a sign, thus notifying the driver of any upcoming signs. This not only ensures road safety, but also allows the driver to be at little more ease while driving on tricky or new roads. It describes the characteristics and requirements and also difficulties between the road sign identification and recognition of the road signs. Objectives of the study: Some of the our work objectives includes: • To understand the properties of road and traffic signs and their implications for image processing for the recognition task. • To understand colour, spaces and colour space conversion. • To develop robust colour segmentation algorithms that can be used in a wide range of environmental conditions. To identify the most appropriate approach for feature extraction from road signs. • To develop an appropriate road sign classification algorithm. • To evaluate the performance of the afore mentioned methods for robustness under different conditions of weather, lighting geometry, and sign. Motivation of the study The Smart Road Sign Detection system lies in its potential to significantly enhance road safety and traffic management. With the increasing number of traffic accidents caused by missed or misunderstood road signs, such a system provides a reliable solution by ensuring timely detection and recognition of signs, even in challenging conditions like poor visibility or inclement weather. Additionally, it supports the growing demand for autonomous vehicles by enabling real-time sign recognition, crucial for navigation and adherence to traffic rules. By reducing driver distraction and compensating for human error, the system not only fosters safer driving but also contributes to efficient traffic flow. Moreover, the use of cost-effective technologies like Arduino and sensors makes it accessible for implementation in regions with limited infrastructure, ultimately saving lives and promoting technological innovation in intelligent transportation systems. 2. Previous research work According to the paper 1, The main work of proposed system is to detect the sign boards like stop board. For this, system will always trying to determine between the lower and upper range of the red colour and a rectangle be formed on the red signals. The rectangle formed on the red light signal has a fixed area by the use of that area system creates a signal that control the GPIO pins of the raspberry pi. Motor driver IC l293d is used to control the motor, one motor driver IC can control only two motors. So the proposed system use done l293d that is enough to control the motors. In the paper 2-4, they have proposed Neural network technique for road sign recognition. The Neural network classification is time consuming task but provided best result for identification of road sign. It gives best result for colour and shape detection. This performance shows 90% accuracy for classification. the colour detection is done by HSV transformation and YCbCr colour space. Shape detection based on region properties which is used for labelled region. Classification is based on feed forward network and train the image using by input neuron of YCbCr features of 30 x 30 image pixel range. Feed forward network and nearest neighbour classification which gives more robust and accurate results such as HSV image, binary image. The goal of the paper 5 is to develop an efficient TSDR system based on Malaysian traffic sign dataset. In the image acquisition stage, the images were captured by an on board camera under different weather conditions and the image preprocessing was done by using RGB colour segmentation. The recognition process is done by SVM with bagged kernel which is used for the first time for traffic sign classification. The developed system has shown promising results with respect to the accuracy of 95. 71%, false positive rate (0. 009), and processing time (0. 43 s). The recognition performance is evaluated by using ROC curve analysis. To improve our environment. ITS’s have considerable potential to be a future commercial success. These systems are also closely linked to other major emerging technologies; the internet, mobile data services, smart sensors, artificial intelligence, position technologies, geographical information systems (GIS). Road and traffic signs have been designed to be principally distinguishable from the natural and/or man-made backgroundsRoad 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 visibility of traffic signs is affected by the weather conditions like fog, rain, clouds and snow, and other parameters like local light variations such as the direction of light, the strength of the light depending on time of the day and season, and shadows generated by other objects. The output of the detection stage is a list of candidate objects that could be probable road signs. This list is forwarded to the recognizer for further evaluation, and then to the classifier to decide whether the objects in the list are either rejected objects or road signs, and in this case the classifier responds with a sign code. Neural networks are a suitable alternative for recognition and classification of road signs. There are two distinct advantages of using neural networks. First, the input image does not have to be transformed into another representation space. Second, the result of the classification depends only on the correlation between the network weights and the network itself if the network topology is assumed to be chosen from the beginning 6-8. German Traffic Sign Detection Benchmark (GTSRB), Convolutional and Neural Networks (CNN) analyse the large quantity of data accuracy in its classification 9. Detection of thresholds and its shape are used to find the traffic sign absolutely using installed camera 10 3. Proposed work The entire proposed block diagram is shown in figure 1. Camera OV7670 and Arduino are being put to use, the connections are made accordingly. The camera senses the image and the same is displayed on the screen with the help of the Arduino. Images are taken at specific intervals in real time as the singing board is visible. Image acquisition obtains high-quality images. The image acquisition process takes place in a certain way: (1) Image capture: The image is captured using a camera, scanner, or other imaging device. (2) Image processing: The captured image is processed to enhance its quality, remove noise, and correct any distortions. The processed image is stored in a digital format, such as JPEG, TIFF, or RAW. The stored image is transmitted to other devices or locations for further processing, analysis, or display. Images captured by the camera are to be detected. The Machine learning algorithm and Deep learning algorithm, Convolution Neural Network (CNN) is deployed. Edge detection is done to the image. Post detection of the sign the image has to be recognized so as to guide the user, the signs are previously stored in a folder which can be accessed through OpenCV. The detected sign is compared to the stored image of the same sign. The user is being alerted and the vehicle is being instructed as to what has to be next step when driving. PWM is generally more about controlling hardware actions based on the image recognition results. PWM signals could theoretically be used to adjust the exposure of the camera module, particularly when there's a need for improved visibility of road signs in different lighting conditions. Figure 2 and 3 shows the implemented road detection robot and circuit connection respectively. The connecting camera sensor to the board for live image generation process Steps are shown below: Step 1: Voltage Divider for XCLK Pin The external clock pin of the OV7670 camera requires a 3. 3V signal. Voltage Divider Calculation is done using formula is: Vout=Vin×R2R1+R2V₎ₔₓ = V₈₍ R2R1 + R2Vout=Vin×R1+R2R2 Step 2: The OV7670 uses I2C to receive configuration data such as resolution, pixel format, and other settings. Step 3: The VSYNC signal from the OV7670 indicates when a new frame is starting. This is a 3. 3V signal, can connect it directly to the Arduino pin 2 without the need for a voltage divider. Step 4: The PCLK (Pixel Clock) is used to synchronize reading pixel data from the camera. O PCLK pin of the camera to Arduino pin 12. Step 5: The OV7670 camera requires 3. 3V power. We use the 3. 3V output pin from the Arduino to power the camera. Step 6: The pin resets the camera. To keep it running continuously, we can tie this pin to 3. 3V PWDN pin powers down the camera. To keep the camera on, connect