Replaces standard loss functions to better handle small or multi-scale objects.
Published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , this paper focuses on remote sensing and landslide detection using a modified YOLOv5/v10-style architecture. Full Text Access: Available via IEEE Xplore. Ls Models (10) mp4
While based on YOLOv8, this "LS" (Lightweight and Scalable) variant is highly cited for its use of Multi-Scale Ghost Convolution (MSGConv) and efficiency gains of up to 55% FPS. Full Text Access: View the full paper on ResearchGate . Key Technical Features of LS-Models Replaces standard loss functions to better handle small
Integrated into the neck or head of the network to capture global context without the heavy computational cost of standard transformers. While based on YOLOv8, this "LS" (Lightweight and
Available for request or viewing on ResearchGate .
In these "Lightweight" (LS) models, the following components are typically highlighted in the full papers: