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Accurateinfosolution/
Suspicious_activity_detection_Yolov11_Custom

Name: Suspicious_activity_detection_Yolov11_Custom
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Custom YOLO Object Detection Model For Suspicious Activity Detection

This repository contains a custom-trained YOLO-based object detection model designed to detect various objects related to safety and surveillance scenarios, such as Assault, Fighting, Gun, Kidnapping, and more.

Overview
Features
Setup
Usage
Model Details
Results
Limitations
Acknowledgments

Overview

The custom YOLO model is capable of detecting the following classes:

Assault
Fighting
Gun
Kidnapping
Knife
People
Police
Prisoner
Theft/Robbery
Time Bomb

The model is optimized for real-time inference and is suitable for applications in:

Public Safety Monitoring
Surveillance Systems
Security Applications

Features

Real-Time Detection: Utilizes YOLO’s efficient architecture for quick detection.
Custom Classes: Trained on a tailored dataset specific to safety and surveillance.
Confidence Thresholding: Outputs only high-confidence predictions for accurate results.
Visualizations: Overlay detected objects on video feeds or images.

Setup

Prerequisites

Python 3.7+
ultralytics package for YOLO
A GPU (recommended for faster inference)

Installation

Clone this repository:

git clone https://huggingface.co/Accurateinfosolution/Suspicious_activity_detection_Yolov11_Custom
Install dependencies:

pip install ultralytics
Add your trained YOLO model:
- Place your Suspicious_Activities_nano.pt file in the project directory.

Usage

Running Predictions

Run the detection script on a video or image source:

python predict.py

Prediction Script: `predict.py`

The script uses the YOLO framework to load the best.pt model and detect objects:

python

from ultralytics import YOLO

# Load the custom YOLO model
model = YOLO("Suspicious_Activities_nano.pt")

# Perform prediction
model.predict(source="0", show=True, conf=0.6)

Replace "0" with:

A webcam source (0 or 1).
A path to an image/video file.

Model Details

Architecture: YOLO (You Only Look Once)
Classes: 10 custom classes (see Overview)
Confidence Threshold: 0.6
Pre-trained Weights: Fine-tuned on YOLO’s default weights.

Results

Sample output of the model includes bounding boxes for detected objects with labels and confidence scores. Images or video streams are displayed with real-time overlays.

Limitations

Dataset Dependency: The accuracy is influenced by the quality of the training dataset.
Environmental Constraints: Performance may vary under poor lighting or occlusion.
Class Generalization: May not generalize well to scenarios outside its training scope.

Acknowledgments

YOLO Framework: Ultralytics
Custom Dataset: Annotated with bounding boxes for the 10 custom classes.
Contributors: Shreyanth(mailto:shreyanthhg1427@gmail.com)

Accurateinfosolution/Suspicious_activity_detection_Yolov11_Custom