Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
Year: 2023
Type: article
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.
Source: Applied Sciences
DOI: 10.3390/app13095499
Institutions: Vicomtech, University of Naples Federico II
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,558
OpenAlex ID: W4367297973