Image Recognition Method for Power AR Emergency Communication Based on End-Side Multi-Source Data Fusion and Edge Collaboration

In this paper, we propose a power AR emergency communication image recognition method based on end-side multi-source data fusion and edge synergy to solve the problems of real-time, accuracy, and resource constraints in power communication emergency scenarios. Specifically targeting challenges including real-time response requirements under communication-restricted conditions, complex scene variations, and computational resource limitations of wearable devices, the method adopts the cloud–edge–end collaborative architecture: the end-side collects multimodal image data through wearable AR devices and utilizes multi-scale decomposition and reconstruction techniques for preliminary processing and content complementation; the edge-side deploys the EdgeYOLO model, which is based on the Fourier federated learning method to aggregate and optimize the image features in the frequency domain, realizing the dynamic collaboration of multi-devices; and the cloud-side completes the depth of the CloudYOLO model through deep CloudYOLO model completes the deep computation and global optimization to improve the model performance and adaptability. Experimental results on the COCO dataset demonstrate that our method achieves a 15.7% improvement in mAP while maintaining real-time performance, along with a 55.1% reduction in response time.