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Develop a security surveillance system using YOLOv7 to detect weapons in real-time from CCTV feeds. Train the model with weapon datasets and trigger alerts when detected.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI identifies weapons like guns or knives in surveillance footage using custom-trained models.
Use Python with OpenCV and the YOLOv7 model to train and detect weapons accurately in real-time.
Build a real-time system that detects and highlights weapons from live video streams.
Log weapon detections with timestamps and images into a security database for alerts and reports.
Develop a GUI to display live feed, detection alerts, and captured weapon data effectively.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Weapon Detection using YOLOv7 is a computer vision application that uses deep learning to identify weapons such as guns and knives in images or video feeds. It is crucial for enhancing surveillance, ensuring public safety, and supporting law enforcement agencies.
Course Introduction and Features
Uses YOLOv7 for real-time weapon detection in images and videos
Plays a vital role in preventive security and crime response
Setting up the Python development environment involves installing the necessary tools, libraries, and dependencies required for developing and running the weapon detection model. It forms the foundation for building and executing machine learning projects efficiently.
Installing Python
VS Code Setup for Python Development
The Weapon Detection Project Overview provides a high-level summary of the goals, methodologies, and outcomes of using YOLOv7 for real-time weapon recognition. It highlights the project’s relevance in security and threat prevention applications.
Weapon Detection Project Overview
Summarizes the purpose and scope of the weapon detection system
Emphasizes security and public safety as key goals
Provides a scalable solution for weapon threat mitigation
Google Colab is a cloud-based platform that enables training of deep learning models with ease. This setup process allows users to leverage free GPU resources for developing the YOLOv7 weapon detection model without needing local computational power.
Setup in Google Colab for Weapon Detection Model Training
Utilizes Google Colab as a cloud-based development environment
Enhances productivity with auto-save and crash recovery features
Mounting Google Drive on Colab facilitates seamless access to datasets, model checkpoints, and output results. It is an essential step for organizing project files and managing data in the cloud-based development workflow.
Mounting Google Drive on Google Colab
Connects Google Drive to Colab for cloud file access
Essential for collaborative and long-term projects
The Sohas Weapon Detection Dataset is a curated collection of images labeled with various types of weapons. It is used to train and evaluate the YOLOv7 model to accurately detect and classify weapons in different scenarios.
Working with Sohas Weapon Detection Dataset
Provides a labeled dataset of weapons like guns and knives
Contributes to safer environments through AI-driven monitoring
This step involves copying the official YOLOv7 GitHub repository and installing all necessary packages and dependencies. It ensures the environment is ready to run the model, modify code, and begin training or inference tasks.
Cloning YOLOv7 Repository and Installing Packages
Clones the official YOLOv7 repository from GitHub
Ensures compatibility with PyTorch and CUDA for GPU use
Forms the foundation for developing a custom detection pipeline
Dataset visualization is the process of exploring and displaying sample images from the dataset to understand its structure, class distribution, and annotation quality. It is vital for identifying issues and improving dataset quality.
Dataset Visualization for Weapon Detection
Displays sample images from the weapon detection dataset
Assists in detecting corrupted or mislabeled data
Improves overall dataset integrity and training readiness
Dataset visualization is the process of exploring and displaying sample images from the dataset to understand its structure, class distribution, and annotation quality. It is vital for identifying issues and improving dataset quality.
Dataset Splitting for Weapon Detection
Divides the dataset into training, validation, and test sets
Prepares data for efficient use in training pipelines
This walkthrough provides an in-depth explanation of the YOLOv7 model architecture, configuration, and training pipeline. It helps users understand how the model processes images and predicts weapon locations and classes.
Detailed Walkthrough of YOLOv7 Code for Weapon Detection
Explains the YOLOv7 model architecture and layers
Enables users to train, fine-tune, and deploy YOLOv7 effectively
Model training involves feeding the dataset into the YOLOv7 network, allowing it to learn patterns and features associated with weapons. Training aims to optimize the model’s accuracy and robustness in detecting weapons in various environments.
Training the YOLOv7 Model for Weapon Detection
Loads the annotated weapon dataset into the training pipeline
Prepares the final trained model for inference and deployment
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
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Highly recommended for small teams who seek to upgrade their time & perform.
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