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YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones.

Gokhan Kucukayan¹, Hacer Karacan²

¹Informatics Institute, Gazi University, 06680 Ankara, Turkey.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This study presents YOLO-IHD, a new AI model for autonomous indoor human detection using drones. It enhances search-and-rescue operations with improved accuracy in complex environments.

Keywords:

UAV YOLO-IHD indoor drone real-time object detection

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision
Unmanned Systems

Background:

The integration of AI and autonomous capabilities is crucial for unmanned systems.
Accurate human detection in indoor environments is challenging for drones.

Purpose of the Study:

To develop a novel deep learning model for autonomous indoor human detection using drones.
To improve the accuracy and reliability of human detection in complex indoor settings.

Main Methods:

Utilized the You Only Look Once (YOLO) deep learning framework.
Developed a new model (YOLO-IHD) trained on a unique dataset from indoor drone footage.
Incorporated optimized convolutional layers and an attention mechanism.

Main Results:

Achieved significant improvements in indoor human detection accuracy.
Demonstrated enhanced performance in autonomous monitoring and search-and-rescue.
Showcased real-time detection capabilities with an accelerating processing library and custom drone.

Conclusions:

The YOLO-IHD model offers a significant advancement for indoor human detection via drones.
The model is crucial for critical applications like disaster response and indoor rescue.
This research provides a foundation for future enhancements in drone-based human detection.