Weapon Detection: AI surveillance for high-risk zones—real-time, scalable threat detection to enhance public safety
Project Overview
This project developed a weapon detection system using YOLOv5, specifically designed for security applications in high-risk areas such as schools, airports, and public venues. A custom dataset sourced from YouTube videos was utilized to ensure diversity in weapon types and real-world scenarios. The primary goal was to enhance security measures and enable rapid threat identification in sensitive environments.
Problem Statement
The rise in security threats in public and sensitive areas has increased the demand for more advanced weapon detection systems. Existing solutions often fail to maintain high accuracy in dynamic, fast-paced environments, leading to slower response times and potential safety risks. A scalable, reliable weapon detection system was needed to be deployed in various high-risk locations without compromising performance.
Key Findings
- Effective Feature Identification: Successfully pinpointed key visual characteristics of weapons across a wide range of conditions—including various types, angles, and lighting scenarios—to enhance detection accuracy.
- Model Robustness: The model demonstrated strong performance in differentiating between actual weapons and harmless objects, significantly reducing the rate of false positives.
- Real-Time Detection: YOLOv5’s high-speed object detection enabled the system to identify threats instantly, offering critical advantages in high-risk surveillance scenarios.
Implemented Solution
Trained YOLOv5 on a custom dataset with diverse weapon types, applied data augmentation, created a deployment pipeline, and implemented real-time alerts for fast responses:
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Custom Dataset Training:
The YOLOv5 model was trained on a comprehensive dataset containing a variety of weapon types in diverse environments, ensuring adaptability and reliability.
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Data Augmentation Techniques:
Applied a range of augmentation strategies to improve model generalisation, enabling consistent performance across different lighting, backgrounds, and object orientations.
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Deployment Pipeline:
Developed an integration-ready deployment pipeline for existing surveillance systems, ensuring hassle-free adoption and scalability.
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Real-Time Alert System:
Implemented an instant alert mechanism that automatically notifies security teams upon detection of a weapon, supporting proactive threat management and emergency readiness.
Results
The weapon detection system achieved a 90% accuracy rate in identifying firearms and other weapons, enabling significantly faster threat recognition and response. Real-time alerts facilitated immediate action by security personnel, reducing risk and improving on-site safety. The solution’s scalable architecture allows for easy integration into existing CCTV or surveillance systems, supporting deployments in schools, transportation hubs, and public gatherings. This AI-powered approach to threat detection represents a major advancement in proactive, real-time public safety infrastructure.