YOLO-Seg Model with Tiling Movement for Detecting Paddy Parcels using High-Resolution Drone Image

This study aims to develop a system that combines object detection and segmentation capabilities in high-resolution images using the YOLO-Seg model for paddy parcel monitoring based on drone imagery. For the first time, we propose a novel tiling movement technique and systematically analyze optimal overlap rates to reduce tile boundary errors that occur in large-scale drone image processing.本研究旨在开发一套基于无人机影像的稻田地块监测系统，该系统结合目标检测与图像分割能力，采用YOLO-Seg模型处理高分辨率图像。本研究首次提出一种新颖的分块移动技术，并系统分析了最优重叠率，以降低大规模无人机图像处理过程中产生的分块边界误差。