DeepSafe:Two-level deep learning approach for disaster victims detection

Background

Efficient disaster victim detection (DVD) in urban areas after natural disasters is crucial for minimizing losses. However, conventional search and rescue (SAR) methods often experience delays, which can hinder the timely detection of victims. SAR teams face various challenges, including limited access to debris and collapsed structures, safety risks due to unstable conditions, and disrupted communication networks.

Methods

In this paper, we present DeepSafe, a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset. DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories. Subsequently, Detectron2 is used to precisely locate and outline the victims.

Results

Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection. The model effectively identified and located victims under the challenging conditions presented in the dataset.

Conclusion

DeepSafe offers a practical tool for real-time disaster management and SAR operations, significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.