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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Real-Time Person Detection in Wooded Areas Using Thermal Images from an Aerial Perspective.

Oscar Ramírez-Ayala¹, Iván González-Hernández¹, Sergio Salazar¹

¹Aerial and Submarine Autonomous Navigation Systems Program, Cinvestav, Mexico City 07360, Mexico.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This study introduces a new thermal imaging dataset and deep learning models for detecting people in wooded areas from aerial views. This approach improves search and rescue operations by overcoming challenges like small object size and occlusion.

Keywords:

CNN UAV robust control

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Detecting people from aerial platforms in wooded environments for search and rescue is challenging due to small object dimensions and occlusions.
Existing RGB datasets for person detection do not adequately address occlusion scenarios.

Purpose of the Study:

To develop a novel thermal image dataset specifically designed for detecting occluded persons from aerial perspectives.
To create and evaluate Convolutional Neural Network (CNN) deep learning models for real-time aerial person detection.

Main Methods:

Development of a specialized thermal image dataset incorporating occlusion scenarios.
Implementation of CNN-based deep learning models for person detection.
Utilizing a quadcopter prototype with altitude control for aerial data acquisition.

Main Results:

The proposed thermal imaging approach effectively highlights occluded individuals, improving detection capabilities.
CNN models trained on the new dataset demonstrate enhanced performance in identifying people in challenging wooded environments.

Conclusions:

Thermal imaging combined with deep learning offers a promising solution for aerial person detection in occluded, wooded search and rescue scenarios.
The developed dataset and models advance the state-of-the-art in real-time aerial surveillance for critical operations.