# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 1 Introduction  
Situational Awareness (SA) is critical for First Responders (FRs) during rescue operations. FRs must have a clear understanding of the location of individuals in need, any potential risks, and any other essential factors crucial to properly and safely perform their duties. The use of Artificial Intelligence (AI) and advances in object detection technology can greatly enhance FRs' SA by identifying and highlighting key elements, reducing their cognitive burden, and improving their perception. However, these improvements are effective only in well-lit environments, while, in real-world scenarios, FRs often face challenging conditions, such as smoke, dust, or limited visibility, which can impair both human and AI perception. To overcome these challenges, it is necessary to utilise technology and equip FRs with tools and resources capable to improve their SA in all situations and conditions. One critical aspect of enhancing FRs' perceptual ability is to improve their visibility under adverse conditions, such as overexposed and underexposed scenarios. Traditional approaches to image restoration and exposure correction rely on image histogram adjustments. Histogram-based methods operate on the statistical distribution of pixel intensities in an image. These methods modify the histogram to make the image visually appealing and better expose the features in the scene [1; 2; 3; 4]. Other techniques, related to Retinex theory [5], have also found wide application, both within and outside of learning-based methodologies [6; 7; 8; 9; 10; 11]. According to Retinex theory, the perceived colour of an object depends on both the spectral reflectance of the object and the spectral distribution of the incident light. Retinex-based exposure correction algorithms recover the original reflectance of the scene by removing the effects of the illumination. Further research has shown the effectiveness of High Dynamic Range (HDR) based techniques for exposure correction. The most common technique for creating HDR images is to exploit the information of a stack of bracketed exposure Low Dynamic Range (LDR) images [12; 13; 14; 15]. Other techniques reconstruct an HDR image from a single LDR image [16; 17; 18; 19; 20]. Despite significant advances in exposure correction techniques, it is essential to note that existing methods are not optimised for rescue scenarios and may not meet the specificneeds of FRs. To address this gap, we present a novel pipeline that integrates Deep Learning (DL) and Image-Processing (IP) techniques specifically tailored to improve the operational abilities of rescuers. Our method is designed to meet three main requirements: detailed feature recovery, enhanced object detection, and efficiency. Detailed feature recovery refers to the model's ability to enhance a scene by revealing hidden features, whereas improved object detection capabilities refer to the model's capacity to enhance other DL techniques by acting as an intelligence amplification (IA) layer. Furthermore, the pipeline has been designed to handle the high-stress, fast-paced nature of rescue situations, where accurate and quick information is crucial. As a proof of concept, the pipeline has been tested on a data set created in the framework of the Horizon 2020 project, \"first RESPonder-Centered support toolkit for operating in adverse and infrastrUcture-less EnviRomments\" (RESCUER), during the Earthquake Pilot performed in Weeze, and organised by I.S.A.R Germany (International Search and Rescue organisation). The paper is structured as follows. In Section 2, we describe the technical details of the pipeline, including exposure correction, segmentation, and fusion techniques, indicating the DL and IP features involved in the process. We also introduce the data set used, and how we collected the training data. In Section 3, we present different metrics of model performance validation over the data set constructed in the Weeze Earthquake Pilot.