Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach-Reference-Cited by-同舟云学术

Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach

Published:2023-04-28 Issue:9 Volume:13 Page:5499
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Di Maro Alberto¹²^ORCID,Azpiroz Izar¹^ORCID,Biain Xabier Oregui¹,Longo Giuseppe²,Olaizola Igor Garcia¹^ORCID

Affiliation:

1. Vicomtech Foundation, Basque Research Technology Alliance (BRTA), 20009 Donostia, Spain

2. Department of Physics, Monte Sant’Angelo Campus, University of Naples Federico II, Via Cinthia, 80126 Naples, Italy

Abstract

This paper presents a Deep Learning (DL) and Image-Processing (IP) pipeline that addresses exposure recovery in challenging lighting conditions for enhancing First Responders’ (FRs) Situational Awareness (SA) during rescue operations. The method aims to improve the quality of images captured by FRs, particularly in overexposed and underexposed environments while providing a response time suitable for rescue scenarios. The paper describes the technical details of the pipeline, including exposure correction, segmentation, and fusion techniques. Our results demonstrate that the pipeline effectively recovers details in challenging lighting conditions, improves object detection, and is efficient in high-stress, fast-paced rescue situations.

Funder

EU Horizon 2020

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/9/5499/pdf

Reference40 articles.

1. Enhancement of low exposure images via recursive histogram equalization algorithms;Singh;Optik,2015

2. Contextual and variational contrast enhancement;Celik;IEEE Trans. Image Process.,2011

3. Multi-exposure optimized contrast and brightness balance color image enhancement;Bhandari;Digit. Signal Process.,2022

4. Goyal, V., and Shukla, A. (2021). Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS 2020, Springer.

5. The retinex theory of color vision;Land;Sci. Am.,1977

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