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

• 🧠 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

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)

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

Preprocessing Steps:

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

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)

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

• 📤 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

streamlit run app.py

• Left: Uploaded Image
• Right: Pistol Detection Results

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

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

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

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

python telegram_bot.py

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

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

streamlit run app.py

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

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

pip install -r requirements.txt

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

📂 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

• 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.

• PyTorch
• Torchvision

• OpenCV
• PIL
• NumPy

• Matplotlib
• Seaborn

• Streamlit
• Telegram Bot API

• dotenv
• tqdm

• 🚀 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

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

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

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

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