计算机应用与软件2024，Vol.41Issue(8)：155-161,167,8.DOI:10.3969/j.issn.1000-386x.2024.08.022
优化特征融合的多尺度遥感图像目标检测方法
MULTI-SCALE REMOTE SENSING IMAGE TARGET DETECTION METHOD BASED ON OPTIMIZED FEATURE FUSION
摘要
Abstract
A DAFFNet remote sensing image object detection algorithm is proposed to solve the problem of low accuracy of multi-scale object detection in the remote sensing image scene.Based on SSD,the algorithm was improved in three aspects.We designed a group-based feature fusion method to enhance the ability to acquire multi-scale feature information.A multi-dimensional feature optimized method based on the attention mechanism was introduced to solve the difficulty of target classification in a complex background.The focal loss was used as a new bounding box confidential loss function to make the model focus on the positive samples that were difficult to classify,so as to improve the interference caused by the imbalance of positive and negative samples to target classification.The model was evaluated on the remote sensing public dataset NWPU VHR-10.The experimental result shows that the proposed algorithm improves the mean average precision by 5.1 percentage points compared with the original algorithm,which can effectively increase the object detection accuracy of remote sensing image.关键词
遥感图像/目标检测/分组特征融合/多维度特征优化/注意力机制Key words
Remote sensing images/Object detection/Group-based feature fusion/Multi-dimensional feature optimization/Attention mechanism分类
计算机与自动化引用本文复制引用
张昊,刘凤,谭富祥,钱育蓉..优化特征融合的多尺度遥感图像目标检测方法[J].计算机应用与软件,2024,41(8):155-161,167,8.基金项目
国家自然科学基金项目(61966035) （61966035）
国家自然科学基金联合基金项目(U1803261) （U1803261）
自治区科技厅国际合作项目(2020E01023) （2020E01023）
智能多模态信息处理团队项目(XJEDU2017T002) （XJEDU2017T002）
自治区研究生创新项目(XJ2019G069,XJ2019G071,XJ2020G074). （XJ2019G069,XJ2019G071,XJ2020G074）
