Ls Models (10) Mp4 Access

Integrated into the neck or head of the network to capture global context without the heavy computational cost of standard transformers.

Available for request or viewing on ResearchGate .

Replaces standard loss functions to better handle small or multi-scale objects. Ls Models (10) mp4

Reduces parameters and FLOPs while maintaining feature extraction quality.

In these "Lightweight" (LS) models, the following components are typically highlighted in the full papers: Integrated into the neck or head of the

This paper introduces a lightweight model designed for underwater vehicles, utilizing Region Scaling (RS) loss and self-attention mechanisms to improve small-object detection in complex environments.

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. Full Text Access: Available via IEEE Xplore

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