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.
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