基于幾何表征學習的弱監(jiān)督旋轉(zhuǎn)目標檢測
打開文本圖片集
中圖分類號：TP391 文獻標志碼：A 文章編號：1671-8755（2025）02-0094-11
Abstract： To address the challnges of high complexity and annotation costs in general rotated object detection for remote sensing images， this paper proposed a weakly supervised rotated object detection model based on geometric representation learning.The proposed method utilized only horizontal bounding box annotations for training and employed a dual-branch architecture with shared backbone and neck networks.The weakly supervised branch learned the position，aspect ratio，and scale consistency of rotated bounding boxes from horizontalannotations，while theself-supervised branch enhanced rotation consistency.To improve feature representation and contextual interaction，the model introduced a shalow feature enhancement module and proposed a geometric vector representation for rotated bounding boxes， thereby improving the accuracy of rotation consistency learning. For bounding box regression，a distance loss based on vertex coordinates （FPD Loss）was introduced to reduce the sensitivity of size regression to angle precision. Experimental results on the public remote sensing datasets DOTA and DIOR -R demonstrate that the proposed model achieves accuracies of 79.33% and 58.50% ，respectively，outperforming the H2RBox algorithm by 4.8 and 1.5 percentage points. The proposed method improves accuracy while reducing computational cost and complexity under the condition of horizontal bounding box annotations， providing a novel solution for rotated object detection in remote sensing images.
Keywords： Remote sensing image;Weakly supervised learning；Rotated object detection; Vector representation；Feature enhancement
隨著遙感圖像及相關技術的快速發(fā)展，目標檢測已作為遙感應用中的核心技術之一，廣泛應用于環(huán)境監(jiān)測[1]、城市規(guī)劃[2]、災害響應[3]等領域。（剩余15385字）