# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 5 Conclusions  
In this study, we propose a pipeline of Deep Learning (DL) and Image-Processing (IP) techniques to improve the Situational Awareness (SA) of First Responders (FRs) in adverse lighting conditions. The pipeline consists of three modules: Exposure Correction (EC), Exposure Segmentation (ES), and Image Fusion (IF). EC aims to adjust the exposure of the acquired images to an optimal level, ES separates the images into regions of interest based on their exposure levels, and IF combines both the EC and ES results to produce the final image. The proposed method offers a solution to the challenges faced by FRs to enhance their operational capacity and allow them to make more informed decisions in high-pressure and fast-paced rescue scenarios. While our method has shown improvements in image detail recovery, object detection performance, and fast processing, there is still room for improvement to address its limitations and expand its applicability. In particular, while our ES module strikes a balance between image enhancement and fast inference, we recognize that there is potential to explore adaptive thresholds or learning-based segmentation to improve its effectiveness in complex situations. Additionally, generating exposure segmentation masks with high precision is essential for ensuring the accuracy and reliability of our model. To further enhance the performance and applicability of our method, we plan to modify the pipeline by removing its dependencies on the ES module. Specifically, we aim to use the EC module to generate multiple images with different exposure levels and merge them to create an image with a wider dynamic range. We intend to explore the integration of our pipeline into augmented reality visualization tools like the Hololens, which could offer new possibilities for real-world applications. Through the Modane pilot, we plan to collaborate with professional first responders, including firefighters and medical rescuers, to gather feedback and refine it based on input from actual end-users. In conclusion, the proposed pipeline provides a solution to the challenges faced by FRs under adverse lighting conditions, and the results indicate its potential for further development and implementation in real-world scenarios. Conceptualization, A.D.M. and I.A. ; Formal analysis, A.D.M. ; Funding acquisition, I.G.O. ; Investigation, A.D.M., I.A. and X.O.B. ; Methodology, A.D.M. and I.A. ; Project administration, I.G.O. ; Software, A.D.M. ; Supervision, I.A., G.L. and I.G.O. ; Validation, A.D.M., I.A. and X.O.B. ; Writing--original draft, A.D.M. ; Writing--review & editing, I.A., G.L. and I.G.O. All authors have read and agreed to the published version of the manuscript. This research work was supported by the EU Horizon 2020 Grant Agreement no. 101021836 for the RESCUER (first RESPonder-Centered support toolkit for operating in adverse and infrastUcture-less EnviRoments) project. [[https://cordis.europa.eu/project/id/101021836](https://cordis.europa.eu/project/id/101021836)]([https://cordis.europa.eu/project/id/101021836](https://cordis.europa.eu/project/id/101021836)), accessed on 23 April 2021 (Project Link). Not applicable. We are grateful to all the individuals who participated in the creation of the validation set at the WEEZE earthquake pilot. Their contributions were invaluable in making this research possible. A.D.M. acknowledges a traineeship grant from Vicomtech Foundation. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: