Explainable Attention-Based AAV Target Detection for Search and Rescue Scenarios

Search and rescue (SAR) assumes primary focus during the post-disaster response phase. In recent years, the rapid advances in autonomous aerial vehicle (AAV) target detection technology have opened up new possibilities for SAR operations. However, the images captured by AAV exhibit considerable variation as they dynamically operate at different altitudes, posing challenges for rescue teams in identifying inconspicuous rescue targets. Furthermore, rescuers can hardly trust a detection model with an opaque decision-making process. To this end, we propose an explainable Region of Interest (RoI) attention-based AAV target detection network (RAXNet) capable of detecting small rescue targets with visual explanations. In this model, we first adopt path aggregation network (PANet) as the neck to extract features from different scales. Then, a RoIAttention module is designed to enhance the small target features while providing visual explanations. Specifically, we employ the RoIAlign and nonmaximum suppression methods to obtain region proposals of small targets in the low-level feature layer, followed by an attention-based feature enhancer to focus the extracted region proposals. By combining the enhanced features with the original ones, RAXNet can detect inconspicuous rescue targets and offer corresponding visual explanations, improving model credibility and facilitating rescue efficiency. Finally, we build an AAV visualization system to help rescuers assess disaster sites in real time. The effectiveness and explainability of the proposed method is demonstrated on the VisDrone DET benchmark and RescueNet datasets.