Abstract: Variations in scene complexity and image quality across remote sensing images lead to inconsistent performance when applying pretrained semantic segmentation models. To ensure quality ...

Introduction: Weeds compete with crops for water, nutrients, and light, negatively impacting maize yield and quality. To enhance weed identification accuracy and meet the requirements of precision ...

Introduction: Rising global populations and climate change necessitate increased agricultural productivity. Most studies on rice panicle detection using imaging technologies rely on single-time-point ...

This repository contains the official Pytorch implementation of training & evaluation code and the trained models for Offset Learning & OffSeg. Offset Learning —— An efficient plug-and-play semantic ...

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...

Abstract: The purpose of this paper is to study of how machine learning algorithms, artificial intelligence, semantic segmentation and fine recognition can be used to enhance computer vision in order ...

The Amazon rainforest, a vital global carbon sink, is under increasing threat from rising temperatures, prolonged droughts, deforestation, and wildfires. Timely, accurate monitoring of forest cover is ...