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🔫 Pistol Object Detection using Faster R-CNN (ResNet50 RPN)

🚀 Overview

Pistol Object Detection using Faster R-CNN with ResNet50 RPN is an end-to-end deep learning project built to detect and localize pistols in images for potential safety and security applications.
It leverages state-of-the-art object detection architecture — Faster R-CNN with a ResNet50 Feature Pyramid Network (FPN) — fine-tuned on a custom dataset of pistol images.

The project showcases the full lifecycle of AI solution development:

⚙️ Model Training → 🔍 Object Detection → 🌐 Streamlit Web App → 🤖 Telegram Bot Deployment

🎯 Project Highlights

  • 🧠 Deep Learning Model — Faster R-CNN (ResNet50 backbone) trained for high-accuracy pistol detection
  • 🗃️ Custom Dataset — Curated and annotated dataset of pistols in real-world scenarios
  • 🧩 Transfer Learning — Efficient adaptation from COCO pre-trained weights
  • 🎨 Bounding Box Visualization — Real-time pistol localization with labeled boxes
  • ⚡ Streamlit Web App — Interactive image-based detection interface
  • 💬 Telegram Bot Integration — Chat-based pistol detection for fast mobile access
  • 📈 Performance Optimization — SGD optimizer, StepLR scheduler, data augmentation, and checkpointing

🧩 Model Architecture

The model architecture is based on Faster R-CNN with ResNet50 FPN, a two-stage object detection pipeline:

  1. Region Proposal Network (RPN) – Generates potential bounding boxes
  2. ROI Pooling + Classification Head – Refines proposals and predicts class labels

Architecture Summary:

  • Backbone: ResNet50 Feature Pyramid Network
  • ROI Head: Custom FastRCNNPredictor
  • Loss Function: Multi-task loss (Classification + Regression)
  • Optimizer: SGD (momentum=0.9)
  • Learning Rate Scheduler: StepLR
  • Framework: PyTorch (torchvision)

📦 Dataset & Preprocessing

The dataset contains images of pistols and backgrounds, annotated in COCO-like JSON format.

Component Details
Classes 2 → ['bg', 'Pistol']
Image Size Variable (resized dynamically)
Format JPG

Preprocessing Steps:

  • Image normalization (ToTensor())
  • Data augmentation: random flip, scale, color jitter
  • Label encoding for class mapping
  • Batched DataLoader for GPU efficiency

🧠 Model Training

from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor

model = fasterrcnn_resnet50_fpn(weights='COCO_V1')
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes=2)

🌐 Streamlit Web App Deployment

A clean, interactive Streamlit web interface enables users to upload images and visualize pistol detections in real-time.

🧩 Features

  • 📤 Upload an image (JPG/PNG)
  • ⚙️ Model runs inference with bounding boxes and confidence scores
  • 🖼️ Displays uploaded and detected images side-by-side
  • 🟥 Dynamic red bounding boxes and white labels for detected pistols
  • 🎚️ Adjustable confidence threshold for precision control

▶️ Run the App

streamlit run app.py

🖥️ App Layout & ⚙️ Tech Stack

🧩 App Layout

  • Left: Uploaded Image
  • Right: Pistol Detection Results

⚙️ Tech Stack

  • Frontend: Streamlit
  • Backend: PyTorch
  • Frameworks: torchvision, Pillow, numpy

🤖 Telegram Bot Deployment

A fully functional Telegram Bot allows pistol detection directly from chat — fast, simple, and secure!

⚙️ Commands

  • /start → Welcome and usage info
  • /help → Guide on how to use the bot
  • /about → Model and project details

🧠 How It Works

  1. User sends an image 📸
  2. Bot detects pistol 🔍
  3. Returns the image with bounding boxes 🟥

▶️ Run the Bot

python telegram_bot.py

⚙️ Required Setup

To get started with the Pistol Object Detection system, follow these simple setup steps:

  1. Clone the Repository

    git clone https://github.com/yourusername/pistol-object-detection.git
cd pistol-object-detection

  2. Install Dependencies

    pip install -r requirements.txt

  3. Install Dependencies

    pip install -r requirements.txt

  4. Set Up Telegram Bot (Optional)

  • 🤖 Create a bot using @BotFather on Telegram.
  • 🔑 Retrieve your API Token and add it to a .env file in your project directory: 
    TELEGRAM_BOT_TOKEN=your_token_here
  1. 🚀 Run the Applications

🧠 For Streamlit Web App

Launch the interactive web app to visualize pistol detection in real-time:

streamlit run app.py

🤖 For Telegram Bot

Run the bot to enable pistol detection directly via chat:

python telegram_bot.py

Once running, the bot supports:

  • /start → Welcome message
  • /help → How to use the bot
  • /about → Project and model details

⚙️ Installation

🧩 Clone Repository

git clone https://github.com/yourusername/pistol-object-detection.git
cd pistol-object-detection

📦 Install Dependencies

pip install -r requirements.txt

Required Packages

  • torch
  • torchvision
  • streamlit
  • pillow
  • opencv-python
  • python-telegram-bot
  • numpy
  • python-dotenv

🧱 Project Structure

📂 Pistol Object Detection using Faster R-CNN │ ├── 📄 app.py # Streamlit web app ├── 📄 telegram_bot.py # Telegram bot integration ├── 📄 detection.py # Core detection logic ├── 📄 pistol_object_detector.pth # Trained model weights ├── 📄 requirements.txt # Python dependencies ├── 📄 .env # Telegram bot token ├── 📁 data/ # Dataset directory ├── 📁 outputs/ # Model outputs & results └── 📄 README.md # Project documentation

🧩 Key Learnings

  • Built a custom Faster R-CNN object detector for domain-specific pistol detection.
  • Used transfer learning with fine-tuning for faster convergence.
  • Developed real-time, interactive AI apps using Streamlit and Telegram.
  • Integrated deep learning with communication platforms for instant inference.

🧰 Tools & Frameworks

Category & Tools

Deep Learning

  • PyTorch
  • Torchvision

Data Handling

  • OpenCV
  • PIL
  • NumPy

Visualization

  • Matplotlib
  • Seaborn

Deployment

  • Streamlit
  • Telegram Bot API

Utilities

  • dotenv
  • tqdm

🌍 Future Enhancements

  • 🚀 Expand to detect multiple weapon types (rifles, knives, etc.)
  • 🧩 Integrate YOLOv8 for faster, real-time detection
  • 🧠 Deploy on edge devices (Jetson Nano, Raspberry Pi)
  • 🌐 Create a web-based analytics dashboard for detections
  • 🔔 Enable CCTV-based alert system for proactive monitoring

🧡 Acknowledgements

Special thanks to:

  • 🧠 PyTorch & Torchvision teams for robust deep learning frameworks
  • 💻 Streamlit for enabling rapid AI app deployment
  • 🤖 Telegram Bot API for seamless conversational integration
  • 📚 Faster R-CNN (Ren et al.) researchers for their foundational work in object detection

👨‍💻 Author

Sayam Kumar
📧 sayamk565@gmail.com
🔗 LinkedIn | GitHub

“AI isn’t just about automation — it’s about making intelligence visible.”

🏁 License

This project is licensed under the MIT License.
You are free to use, modify, and distribute it with proper attribution.