helmet and number plate detection power point
- 1. Project Review-I Batch No: CSE_C2 Project Title:Real Time Helmet violation detection system using object detection methods Sno Registration .ID Student Name 1 22A81A05E5 E.MOUNIKA 2 22A81A05H1 M.MOHITH 3 23A85A0518 S.AMRUTHA VARSHINI 4 22A81A05F1 G.MADHU BABU 5 22A81A05G7 M.ENOCH CHANDRAN 6 22A81A05E3 CH.RAMA KRISHNA Project Guide: Mr.ANIL KUMAR REDDY TETALI M.Tech(Ph.d)
- 2. MINI PROJECT REVIEW I- CONTENTS Abstract Introduction Literature Survey Problem Statement in Existing System Proposed System Objectives Project domain Requirement analysis - Functional Requirements - Non-Functional Requirements - Software Requirements - Hardware Requirements
- 3. Abstract: Ensuring road safety is a global priority, with helmet usage playing a crucial role in mitigating fatalities and injuries resulting from motorcycle accidents. This paper presents an innovative method for detecting helmet violations by leveraging object detection algorithms, particularly Convolutional Neural Networks (CNNs). The proposed approach involves training a CNN model onannotated datasets comprising images depicting individuals riding motorcycles, with a focus on identifying instances where helmets are improperly worn or absent, as well as accurately detecting and reading vehicle number plates. The detection process encompasses various stages, including pre-processing, feature extraction, and classification. Through extensive experimentation and evaluation, our system demonstrates robust performance in accurately detecting helmet violations and identifying number plates in real- world scenarios. The integration of CNN-based object detection techniques shows promising results, offering potential for effective enforcement of helmet regulations and augment road safety measures
- 4. Introduction: Ensuring road safety requires motorcyclists to wear helmets to reduce the risk of injury in accidents. Traditional methods for monitoring and enforcing helmet compliance often rely on manual observation by traffic officers, which can be resource-intensive and prone to errors. To address these challenges, our project introduces an advanced helmet recognition system that leverages computer vision techniques and Convolutional Neural Networks (CNNs). The system uses the YOLO (You Only Look Once) algorithm, combined with CNNs, to automatically detect motorcyclists who are not wearing helmets and identify their number plates. This approach enablesmore effective and automated enforcement of helmet regulations.
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- 7. Literature survey: SNo Authors Research Paper Title Publication Methodology Conclusion 1 Bingyan Lin,Fujian Polytechnic of Fuzhou,Chi na Safety Helmate detection based on improved yolov8 IEEE-2024 YOLOv8n-SLIM- CA model,Small Target Detection Layer It improves helmate detection accuracy and efficiency,making it suitable for realtime. 2 Prajwal M.J,Sri Jayachama r ajendra College Detection of Non- Helmate Riders and Extraction using YOLOv2 IJITEE-2019 YOLOv2 and YOLOv3 models,Objectde tec tion,image processing techniques The study created a Automated System using YOLO For detecting non- helmate riders.
- 8. 3 Armstrong Aboah,Bin Wang,Ulas Bagci,Yaw Adu- Gyamfi Real-time Multi-Class Helmate Violaton Detection Using Few- Shot Data Sampling Techniques. AarXiv-2023 Few-Shot Data Sampling Techniques and YOLOv8 model The study created a real-time helmate violation detection system with high accuracy and efficiency for practical use. 4 Elham Soltanikazem i, Elizabeth Arthur, Armstrong Aboah, Bijaya Kumar Hatuwal Fine-Tuning YOLOv5 with Genetic Algorithm For Helmet Violation Detection 2023 IEEE - Applied Imaginary Pttern Recognition Workshop (AIPR) The study focuses on developing a real-time helmet violation detection system using the YOLOv5 model, which is a single- stage object detection model the proposed real- time helmet violation detection system represents a significant advancement in the field. The model developed by the authors achieved a (mAP)
- 9. Problem Statement in Existing System: Inefficiency of Manual Detection: Traditional manual detection by traffic police is slow and increasingly challenging, making enforcement difficult. Resource Intensive: Some systems require expensive hardware or high-resolution cameras, limiting their accessibility and scalability in resource-limited areas. Complexity in Crowded Scenes: Basic models may fail to detect violations accurately in crowded or partially obscured situations. Dependence on High-End Equipment: The need for advanced sensors or high- resolution images restricts the effectiveness of these systems in many regions. Accuracy and Scalability: Machine learning models often struggle with maintaining accuracy in diverse traffic conditions, especially under varying lighting and weather. .
- 10. Proposed System: A hybrid approach using both Convolutional Neural Networks (CNN) and the You Only Look Once (YOLO) algorithm is proposed for detecting helmet violations among motorcyclists. YOLO is utilized for real-time object detection, efficiently identifying and localizing riders in images. Its capability to process images in a single pass allows for rapid detection of potential helmet violations. CNN is employed to further analyze the detected regions, focusing on accurately classifying whether each rider is wearing a helmet. CNN’s ability to automatically extract and learn features from images enhances classification accuracy. This combined approach leverages YOLO’s speed and CNN’s detailed classification to provide a robust, real-time solution for helmet violation detection in various traffic conditions.
- 11. Objectives: • To detect helmet violations among motorcyclists in real-time. • To accurately classify whether riders are wearing helmets using advanced image analysis techniques. • To analyze helmet usage patterns and identify areas or conditions where violations are most frequent. • To provide actionable insights and recommendations to traffic authorities to enhance helmet compliance and safety enforcement. Project Domain: Computer Vision and Deep Learning using Python
- 13. Requirement Analysis: Hardware Requirements: GPU : High performance GPU RAM :8GB Storage:500GB Architecture:32 bit or 64 bit Software Requirements: Operating system :windows Programming language : Python Processor:i5 and i7 Frameworks and Libraries : TensorFlow,OpenCv ,Ultralytics,cvzone,pandas, numpy Web IDE:Google Colab
- 15. Methodology: Data Collection: Gather and annotate a diverse image dataset for helmet and number plate detection. Pre-processing: Standardize and augment images to prepare the dataset. Model Development: Design or fine-tune a CNN model for detecting helmets and number plates. Training: Train the model with optimized techniques to avoid overfitting. Detection: Implement real-time detection for helmets and number plates. Evaluation: Assess model performance with key metrics and real-world testing. Deployment: Integrate the system with traffic infrastructure and ensure scalability. Future Work: Explore improvements in detection capabilities and accuracy.
- 16. Methodology : star t Capture Images or Videos Label data Preprocess data Train model Classify Helmets Detect riders Evaluate model Deploy system Trigger alerts sto p
- 17. Project Plan: Requirement collection and analysis: 20 Days Program Design: 7 Days Coding: 30 Days Testing: 10 Days Deployment: 20 Day