International Research Journal of Engineering and Technology (IRJET) Volume: 10 Issue: 06 | Jun 2023
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Helmet and License Plate Detection using Machine Learning Mr. Meeravali Shaik1, V Uday Kiran2, K Vineeth3, CH Sudheer4 1 Assistant Professor, Dept. Of Computer Science and Engineering, SNIST, Hyderbad, 501301, India 2,3,4B.Tech Scholars , Dept. Of Computer Science and Engineering, SNIST, Hyderbad, 501301, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Over the years, the number of motorcycle
outputs the licence plate number as machine-encoded text. Additionally, a Webcam can be used to implement it. The goal of this study is to create a system that uses CCTV cameras to enforce helmet use. The created system tries to alter risky behaviours, hence lowering accident frequency and severity.
accidents has been rising quickly in many nations. More than 37 million individuals in India ride two wheels. To ensure road safety, a mechanism for the automatic identification of helmet use must be developed. As a result, a unique object detection model that can recognise motorcycle riders is developed utilising a machine learning-based approach. When a rider without a helmet is spotted, the licence plate is retrieved, and an optical character recognition system is used to identify the licence plate number.Using a webcam or a CCTV as input, this application can be used in real-time.
2. RELATED WORK The issue of helmet detection has been addressed in a number of ways during the past few years. In, the authors employ a background subtraction technique to identify and distinguish between moving vehicles. Additionally, they classified human heads with helmets and without helmets using Support Vector Machines (SVM). In, Silva et al. suggested a hybrid descriptor model based on geometric shape and texture data to automatically identify motorcycle riders without helmets. They combined the Hough transform with SVM to find the motorcyclist's head. They also add a multi-layer perception model for the classification of distinct items to their work from [10].
Key Words: Automatic License Plate Recognition (ALPR), Deep Neural Network (DNN), Helmet Detection, Machine Learning, Mean Average Precision (mAP), Optical Character Recognition (OCR), You Only Look Once (YOLO).
1. INTRODUCTION Helmets are the primary piece of safety gear for motorcycle riders. The motorcycle rider is protected from accidents by the helmet. Although wearing a helmet is required in many nations, some motorcycle riders choose not to wear one or wear one inappropriately. Numerous studies in traffic analysis have been conducted in recent years, including those on vehicle detection and categorization and helmet detection. Computer vision technologies, such as background and foreground image detection to segment the moving objects in a scene and image descriptors to extract features, were used to develop intelligent traffic systems. To classify the items, computational intelligence technologies such as machine learning algorithms are also used.
A circular arc detection technique based on the Hough transform is used by Wen et al. They used it on the surveillance system to detect helmets. The flaw in this research is that they rely solely on geometric cues to determine whether a safety helmet is present in the set. Finding helmets requires more than just geometrical traits. suggests a computer vision system with the goal of partially detecting and segmenting motorcycles. Utilizing a helmet detecting device, the presence of a helmet confirms the presence of a motorcycle. The edges are computed on the potential helmet region in order to identify the existence of the helmet. It employs the Canny edge detector. A system to detect moving objects using a k-NN classifier placed over a motorcycle rider's head to categorise a helmet was proposed by Waranusat et al. These models had a cap on the degree of precision that could be attained and were based on statistical data from photographs.
Machine learning (ML) is the area of artificial intelligence where a trained model uses inputs from the training phase to operate autonomously. In order to generate predictions or choices, machine learning algorithms create a mathematical model using sample data, referred to as "training data," and are also utilised in object identification applications. Therefore, a Helmet detection model can be put into use by training with a certain dataset. This helmet detection model makes it simple to identify riders without helmets. The rider's licence plate is clipped out and saved as an image based on the recognised classes.An optical character recognition (OCR) model is given this image, recognises the text, and
© 2023, IRJET
|
Impact Factor value: 8.226
The accuracy of categorization has continued to increase with the development of neural networks and deep learning models. A convolutional neural network (CNN) based approach for object classification and detection was introduced by Alex et al. Despite using CNN, they have poor accuracy in detecting helmets due to restrictions on helmet colour and the presence of several riders on a single biker.
|
ISO 9001:2008 Certified Journal
|
Page 37