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

## TL;DR

This paper introduces YOLO-IHD, a new deep learning model for detecting humans in indoor environments using drones, improving accuracy and real-time performance for applications like search and rescue.

## Contribution

The novel YOLO-IHD model is specifically designed for indoor human detection using drones, with optimized layers and attention mechanisms for better accuracy and real-time performance.

## Key findings

- YOLO-IHD improves human detection accuracy in complex indoor environments.
- The model includes optimized convolutional layers and an attention mechanism for better performance.
- Combined with a processing library, YOLO-IHD enables real-time detection on custom indoor drones.

## Abstract

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications.