DFAF-YOLO: Rescue Action Recognition Network Based on Dynamic Feature Extraction and Multiscale Attention Fusion in UAV Perspective

In the domain of emergency rescue, the capacity to swiftly and precisely identify the actions of rescue personnel is paramount to optimising the efficacy of rescue operations. This study proposes an enhanced real-time action recognition algorithm, termed DFAF-YOLO (Dynamic Feature Extraction and Multiscale Attention Fusion YOLO), in response to the shortcomings of existing algorithms in complex scenarios, particularly their inadequate recognition accuracy. The proposed algorithm incorporates several core optimisations with a focus on feature fusion. Firstly, the CBAM attention module is introduced in the algorithm’s Neck region, with the objective of suppressing redundant information and enhancing core feature transmission through the dual effect of channel and spatial attention. Secondly, the ASFF mechanism is introduced in the Head detection region, with the aim of enabling dynamic alignment and weight adjustment of multi-scale feature maps transmitted from the Neck. This mechanism addresses the issue of information loss caused by scale mismatch. The two modules work in synergy to enhance key feature extraction, with the WIoU loss function being introduced to optimise the accuracy of detection box regression. The experimental validation process has demonstrated that the proposed scheme attains anmAP0.5of 88.6% and an accuracy of 86.5% on the self-built rescue action dataset. In comparison with the base YOLOv8n model, the proposed model demonstrates a 4.4% enhancement inmAP0.5and a 2.9% enhancement in detection accuracy,verifying the effectiveness of the designed optimisations in improving recognition performance under complex rescue scenarios.