Search and Rescue (SAR) operations following natural or anthropogenic disasters are often hindered by smoke, rain, fog and haze. Developing methods that combat Degraded Visual Environments (DVE) and ensure the rapid detection of survivors and rescue personnel after a disaster is crucial to reducing mortality. This paper proposes a novel AI scheme to detect static and moving objects using snapshot NIR hyperspectral data. The proposed model leverages spatial features through object detection to identify objects and, at the same time, applies a semantic segmentation algorithm based on spectral features to validate the presence of firefighters within the detected bounding boxes. The training is optimised for real-time high-performance inference by exporting it to TensorRT. This approach has been successfully demonstrated in various realistic scenarios with an F1-score of 0.923 and 77.9 frames per second.

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Lennert Antson, Arthur Vandenhoeke, Guillem Ballesteros Garcia, and Michal Shimoni, "Innovative AI-based modelling and computing techniques for improving real-time search and rescue operations in impaired environments.," Proc. SPIE PC12528, Real-Time Image Processing and Deep Learning 2023, PC1252803 (Presented at SPIE Defense + Commercial Sensing: May 01, 2023; Published: 13 June 2023); https://doi.org/10.1117/12.2663915.6328611398112.