AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An HGDS-YOLOv8-Pose algorithm embedded with HSAN, GL-CRM, DySample and SCAM modules for robust outdoor human pose estimation, outperforming competing methods including KAPAO-s and YOLOv8n-Pose.
This paper proposes MFA-Pose, an improved YOLO11s-Pose framework that enhances contextual representation and cross-scale feature reconstruction that provides a favorable balance between pose estimation accuracy and model complexity for industrial surveillance applications.
CL-HPE is presented, a lightweight framework for competitive and efficient pose estimation that provides a practical solution in resource-constrained environments and suggests a favorable accuracy–efficiency trade-off among lightweight pose estimators.
Monocular human height estimation is of great value in intelligent security inspection and surveillance applications, but existing methods often suffer from insufficient object detection accuracy and the high computational cost of attention mechanisms. This paper proposes a dual-attention enhanced network based on impr...
This paper proposes a lightweight high-resolution pose estimation network, UG-HRNet, which significantly reduces model complexity while maintaining competitive performance, serving as a practical alternative to existing popular lightweight networks.
A lightweight YOLO-style detector that integrates a PP-HGNetV2 tiny backbone, an enhanced normalization-based attention module (ImNAM), and an improved complete intersection-over-union loss (ImCIoU) shows strong potential for intelligent industrial inspection on resource-constrained platforms, subject to further hardwa...
A novel fall detection algorithm is proposed based on YOLOv11 to address the issues of occlusion sensitivity, small target missed detection, and limited deployment costs in fall detection in complex scenarios. The algorithm introduces a dynamic multi-scale (DM) attention mechanism that integrates dynamic channel attent...
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