OccluSense AI - Advanced Occlusion-Aware Object Detection System for Autonomous Vehicles Using YOLOv8 and Custom NMS Algorithms

Overview

OccluSense AI is a cutting-edge final year project that addresses one of the most critical challenges in autonomous driving systems - accurate object detection in occluded scenarios. This comprehensive Django-based web application combines the power of YOLOv8 object detection with three state-of-the-art Non-Maximum Suppression (NMS) algorithms to deliver superior performance in real-world driving conditions where objects are frequently hidden or partially obscured.

Perfect for students seeking AI final year projects or Python final year projects, this system demonstrates industry-grade implementation of computer vision techniques used in modern self-driving vehicles. The project is built on Django 5.0 and comes with complete source code, making it an ideal final year project with source code for computer science and engineering students.

Key Features and Capabilities

Advanced Object Detection Engine

• Pre-trained YOLOv8 Model: Leverages COCO-pretrained weights for immediate deployment without requiring training infrastructure or datasets
• Three NMS Implementation Methods:
  • Standard NMS: Traditional hard suppression for fast, efficient detection in clear visibility scenarios
  • Soft-NMS: Gaussian decay-based suppression that excels in crowded scenes and overlapping objects
  • DIoU-NMS: Distance-aware IoU computation specifically designed for handling occluded objects by considering center point distances
• Customizable Detection Parameters: Fine-tune confidence thresholds, IoU thresholds, and target classes for optimal results
• Multi-Class Support: Detects vehicles (cars, trucks, buses), pedestrians, bicycles, motorcycles, and traffic lights - all critical for autonomous driving

Multiple Input Processing Modes

• Image Upload: Drag-and-drop interface for single or batch image processing
• Video Analysis: Frame-by-frame video processing with temporal consistency
• Real-time Webcam Detection: Live object detection with adjustable parameters during runtime
• API Integration: RESTful API endpoints for seamless integration with other autonomous driving systems

Comprehensive Analysis and Visualization

• Side-by-Side NMS Comparison: Visual comparison of all three NMS methods on the same input for research and evaluation
• Interactive Dashboard: Chart.js-powered analytics showing detection performance metrics, class distribution, and processing times
• Detailed Detection Reports: Confidence scores, bounding box coordinates, occlusion indicators, and class labels
• Performance Metrics: Real-time FPS monitoring, processing time analysis, and accuracy statistics

Modern Web Interface

• Responsive Dark Theme: Professional UI with glassmorphism effects and modern design patterns
• Bootstrap 5 Integration: Mobile-friendly interface that works on all devices
• Real-time Progress Indicators: Live updates during processing with visual feedback
• Download and Share: Export annotated images and detection results in multiple formats

Real-World Applications

Autonomous Driving Systems

This project directly applies to self-driving car technology, where accurate object detection in occluded scenarios is crucial for safety. The system can identify pedestrians partially hidden behind vehicles, detect cars in heavy traffic, and recognize traffic signals in complex urban environments.

Advanced Driver Assistance Systems (ADAS)

Perfect for implementing collision warning systems, blind spot detection, and pedestrian protection features in modern vehicles. The occlusion-aware detection ensures reliable performance even when objects are partially obscured.

Traffic Management and Monitoring

Can be deployed for intelligent traffic monitoring systems, parking lot management, and traffic flow analysis in smart cities. The multi-NMS comparison feature helps optimize detection parameters for specific deployment scenarios.

Research and Development

Ideal for academic research in computer vision, deep learning, and autonomous systems. The side-by-side NMS comparison feature makes it valuable for comparative studies and algorithm evaluation. This makes it an excellent choice for students looking for AI and ML final year projects or computer vision projects.

Technical Architecture and Implementation

Backend Technologies

Built on Django 5.0, the application follows a modular architecture with separate apps for detection, API, and results management. The core detection engine is implemented in Python using PyTorch 2.0 and Ultralytics YOLOv8, ensuring state-of-the-art performance and compatibility with modern deep learning frameworks.

Custom NMS Algorithms

The project includes hand-crafted implementations of three NMS variants:

• Standard NMS: Implements traditional hard suppression with optimized IoU calculations for real-time performance
• Soft-NMS: Features configurable Gaussian decay with sigma parameter tuning for different scenarios
• DIoU-NMS: Incorporates distance penalty in IoU computation, making it superior for handling occluded objects where bounding boxes overlap but centers are distant

Database and Storage

Uses Django ORM with SQLite for development and supports PostgreSQL for production deployments. All uploaded media, detection results, and performance metrics are stored systematically for easy retrieval and analysis.

API Architecture

The RESTful API provides endpoints for:

• Image and video upload with detection parameters
• Real-time webcam frame processing
• Detection results retrieval with filtering options
• Performance metrics and analytics data
• NMS method comparison on single images

Learning Outcomes for Students

Deep Learning and Computer Vision

• Understanding YOLO architecture and object detection principles
• Implementing and comparing different NMS algorithms
• Working with pre-trained models and transfer learning
• Handling occlusion in computer vision tasks

Web Development Skills

• Building full-stack applications with Django framework
• Creating RESTful APIs for machine learning models
• Implementing real-time features with WebSockets
• Designing responsive user interfaces with Bootstrap

Software Engineering Practices

• Modular architecture and code organization
• Database design and ORM implementation
• File handling and media management
• Performance optimization and metrics tracking

Project Deliverables

When you purchase this final year project from CodeAj Projects, you receive:

• Complete Source Code: Fully commented Python and JavaScript code with modular structure
• Detailed Documentation: Comprehensive README with installation guide, API documentation, and usage instructions
• Database Schema: Pre-configured models and migrations for quick deployment
• Sample Datasets: Test images and videos for immediate testing and demonstration
• Configuration Files: Pre-configured settings for development and production environments

Why Choose This Project?

Industry-Relevant Technology Stack

This project uses technologies actively employed in real autonomous driving companies and research labs. YOLOv8 is one of the most widely adopted object detection frameworks, and Django is a production-grade web framework used by major tech companies.

Comprehensive Implementation

Unlike basic detection projects, this includes advanced features like multiple NMS comparisons, real-time processing, API development, and interactive dashboards - demonstrating full-stack development capabilities that impress academic evaluators and industry recruiters.

Ready for Presentation and Demo

The professional UI, real-time webcam feature, and side-by-side comparison tool make this project excellent for demonstrations and presentations. The interactive dashboard provides visual proof of your technical capabilities.

Extensibility and Customization

The modular architecture makes it easy to extend with additional features like:

• Integration with other object detection models (Faster R-CNN, SSD, DETR)
• Custom dataset training and fine-tuning
• Multi-camera support for 360-degree detection
• Integration with path planning and decision-making systems
• Deployment on edge devices and embedded systems

Perfect for Research Paper Publication

This project provides an excellent foundation for research paper publication. The comparative analysis of three NMS methods in occluded scenarios offers novel insights suitable for academic journals and conferences. Students can extend the work with experimental evaluations, performance benchmarks, and algorithm improvements.

For students specifically looking to publish research papers, CodeAj offers research paper publication support including guidance on paper structure, experimental design, and submission to indexed journals.

Additional Services from CodeAj

Custom Project Implementation

Need modifications or have specific requirements? Our project setup and customization service helps you tailor this project to your exact specifications, whether that means adding new features, changing the technology stack, or adapting it to specific evaluation criteria.

Complete Project Documentation

Along with the source code, we provide comprehensive project reports, research papers, and presentation slides. Our documentation includes theoretical background, implementation details, experimental results, and future scope - everything you need for successful project submission and defense.

One-on-One Mentorship

Get personalized guidance from experienced developers through our mentorship program. We help you understand the code, troubleshoot issues, and explain complex concepts during your project review sessions.

Similar Projects You Might Like

If you are interested in autonomous driving and computer vision, you might also want to explore these related projects:

• Real-time Object Detection WebApp with YOLOv8 and Flask
• AI Vehicle Speed Detection and License Plate Detection System
• Real-time Fire Detection using YOLOv8 and Django
• CheatGuard AI - Classroom Cheating Detection System using YOLOv8

Technical Support and Updates

All projects from CodeAj come with technical support to help you get started. We provide setup assistance, bug fixes, and guidance on understanding the codebase. The project is built with modern, well-maintained libraries ensuring compatibility and long-term usability.

Conclusion

OccluSense AI represents a comprehensive solution for object detection in autonomous driving scenarios with specific focus on handling occlusions - a real-world challenge that sets this project apart from basic detection systems. Whether you are a computer science student looking for an impressive final year project, an aspiring AI engineer building your portfolio, or a researcher exploring advanced computer vision techniques, this project provides the perfect foundation.

With its combination of cutting-edge technology, practical applications, and professional implementation, this project demonstrates expertise in deep learning, web development, and software engineering - skills highly valued in both academia and industry.

For more innovative projects across different domains, visit CodeAj Projects where you will find hundreds of ready-to-use final year projects with complete source code and documentation.