Wider Neighborhood-Aware Attention in Improving YOLOv8n for One-Stage Human Fall Detection

Human fall detection has become a crucial technology in bolstering intelligent surveillance systems. A one-stage human fall detection model based on the YOLO network emerges as an ideal solution for implementation in limited resource environments, supporting real-time operation with faster speed. This work introduces a Wider Neighborhood-Aware Attention (WN2A) module to enhance YOLOv8n performance for one-stage human fall detection on a CPU device. WN2A enables the YOLOv8n network to focus on crucial information within the feature map based on the channel while considering a wider neighborhood area from a spatial point of view. As a result, the proposed WN2A applied on the YOLOv8n network outperforms the other methods based on the mean Average Precision (mAP) of two benchmark datasets. Moreover, the improved YOLOv8n network enables operating at 27.38 frames per second on an Intel Core i7-9750H CPU while providing higher mAP.