dabbledabble-IND-da-air/Threat-Detection-RFDETR

RF-DETR based Threat Detection Model

<a href="https://opensource.org/licenses/MIT"> <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License"> </a> <a href="https://github.com/roboflow/rf-detr"> <img src="https://img.shields.io/badge/RF--DETR-Nano-purple?logo=roboflow&logoColor=white" alt="Model"> </a> <a href="#performance-metrics"> <img src="https://img.shields.io/badge/mAP%4050-84.8%25-darkgreen?style=flat" alt="mAP"> </a> <a href="https://github.com/subh-775/ThreatDetectionYOLO-vs-RF-DETR"> <img src="https://img.shields.io/badge/-code-black?logo=github" alt="Code"> </a>

Transformers for Object Detection

The paradigm has shifted! While CNNs traditionally dominated object detection with faster inference times, RF-DETR (Roboflow's Detection Transformer) has revolutionized the field. This transformer-based architecture not only outperforms CNNs in accuracy but also delivers faster inference for real-time applications.

This repository contains a fine-tuned RF-DETR Nano model specifically trained for threat detection, capable of identifying four critical threat categories with high precision and speed.

Predicted Results

Video Inferencing

<video muted autoplay loop controls src="https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/5Kt3KghZaanzOVaVB6JS9.mp4" width=800></video>

Model Overview

RF-DETR Threat Detection is a specialized computer vision model designed for security and surveillance applications. Built on Roboflow's cutting-edge RF-DETR architecture, this model can accurately detect and classify potential threats in real-time scenarios.

The threat categories are as:

Training Dataset

Our custom threat detection dataset was meticulously curated and annotated to ensure robust model performance across diverse scenarios.

Class Distribution

Sample Annotations (Actual)

The model is trained to detect threats across various scales, from small concealed weapons to larger explosive devices.

Performance Metrics

Training Performance

[image]

The training process demonstrates excellent convergence with:

• —Consistent loss reduction over 50 epochs
• —Stable validation performance indicating good generalization
• —Balanced precision and recall across all threat categories

Validation Results

Test Results

Key Performance Highlights

• —84.8% mAP@50 on validation set
• —Fast inference with RF-DETR Nano architecture
• —Excellent precision for Gun (96.7%) and Grenade (93.1%) detection
• —Consistent recall of 83-85% across all threat categories
• —Robust generalization from validation to test performance

Model Architecture

• —Base Architecture: RF-DETR Nano
• —Input Resolution: 640×640 pixels
• —Backbone: Optimized transformer encoder
• —Detection Head: Custom 4-class threat detection
• —Inference Speed: ~50ms per image (GPU)
• —Model Size: Lightweight for edge deployment

Training Details

Training Configuration

• —Epochs: 50
• —Batch Size: Optimized for available GPU memory
• —Optimizer: AdamW with learning rate scheduling
• —Data Augmentation: Advanced augmentation pipeline for robust training
• —Loss Function: Multi-scale detection loss with class balancing

Training Strategy

1. 1.Progressive Training: Started with lower resolution, gradually increased
2. 2.Class Balancing: Weighted loss to handle class imbalance
3. 3.Data Augmentation: Extensive augmentation to improve generalization
4. 4.Early Stopping: Monitored validation mAP to prevent overfitting

Model Files

• —checkpoint_best_total.pth - Main model weights

Inference Instructions

pip install -q rfdetr==1.2.1 supervision==0.26.1 

• —You can use: video_processing.py to process large videos

• —Below is the script to process a single image

import numpy as np
import supervision as sv
import torch
import requests
from PIL import Image
import os

from rfdetr import RFDETRNano

THREAT_CLASSES = {
    1: "Gun",
    2: "Explosive", 
    3: "Grenade",
    4: "Knife"
}

image = Image.open("Path_to_image")

# pre-trained weights
weights_url = "https://huggingface.co/Subh775/Threat-Detection-RFDETR/resolve/main/checkpoint_best_total.pth"
weights_filename = "checkpoint_best_total.pth"

# Download weights if not already present
if not os.path.exists(weights_filename):
    print(f"Downloading weights from {weights_url}")
    response = requests.get(weights_url, stream=True)
    response.raise_for_status()
    with open(weights_filename, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)
    print("Download complete.")

model = RFDETRNano(resolution=640, pretrain_weights=weights_filename)
model.optimize_for_inference()

detections = model.predict(image, threshold=0.5)

color = sv.ColorPalette.from_hex([
    "#1E90FF", "#32CD32", "#FF0000", "#FF8C00"
])

text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)

bbox_annotator = sv.BoxAnnotator(color=color, thickness=thickness)
label_annotator = sv.LabelAnnotator(
    color=color,
    text_color=sv.Color.BLACK,
    text_scale=text_scale,
    smart_position=True
)

labels = []
for class_id, confidence in zip(detections.class_id, detections.confidence):
    class_name = THREAT_CLASSES.get(class_id, f"unknown_class_{class_id}")
    labels.append(f"{class_name} {confidence:.2f}")

annotated_image = image.copy()
annotated_image = bbox_annotator.annotate(annotated_image, detections)
annotated_image = label_annotator.annotate(annotated_image, detections, labels)
annotated_image.thumbnail((800, 800))
annotated_image

Acknowledgments

• —Roboflow for the RF-DETR architecture
• —Hugging Face for model hosting and distribution
• —PyTorch ecosystem for deep learning framework
• —Supervision library for computer vision utilities

Disclaimer: This model is designed for research purpose only. It's predictions cannot be taken into account for deployment right now.