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malikhannan111/
Weapon_Detector

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Weapon Detection System using YOLOv8

Overview

This project is a weapon detection system built using Deep Learning and Computer Vision. It detects guns in images using the YOLOv8 object detection model. The system is trained on a custom dataset named weapon-detection-test.

Dataset

Dataset Name: weapon-detection-test
Type: Labeled images dataset for weapon detection
Source: Kaggle-based dataset

Model Used

YOLOv8
Pre-trained weights fine-tuned on custom dataset
Trained for 50 epochs

Training Details

Epochs: 50
Task: Object Detection (Gun detection only)
Input: Images
Output: Bounding box around detected gun

Features

Detects guns in images
High-speed inference using YOLOv8
Lightweight and efficient model
Can be extended for real-time detection

Model Files

Trained YOLOv8 weights are used for prediction
Model stored externally due to size limits

Download Model: https://drive.google.com/file/d/1wJDPS5R7bhfx5uJORJ4ZPk4JG1VbvLGG/view?usp=sharing

How It Works

Input image is given to the model
YOLOv8 processes the image
Model detects presence of gun
Output shows bounding box if weapon is found

Limitations

Currently detects only guns (not other weapons)
Works only on images (not real-time video yet)
Performance depends on dataset quality
May fail on unseen environments or angles

Future Improvements

Add real-time video detection
Extend to multiple weapon classes (knife, pistol, etc.)
Improve dataset diversity
Deploy as web or mobile application
Optimize for edge devices (CCTV systems)

How to Use

python

from ultralytics import YOLO
import cv2
# Download the model file from Link, mentioned in  "Model Files" Section.
# Load trained model
model = YOLO("weapon_detector.pt")

# Load image
image_path = "test.jpg"
img = cv2.imread(image_path)

# Run detection
results = model(image_path)

# Show results
results[0].show()

# Optional: save output image
results[0].save("output.jpg")

malikhannan111/Weapon_Detector