南京师大学报（自然科学版）2025，Vol.48Issue(4)：78-86,9.DOI:10.3969/j.issn.1001-4616.2025.04.008
基于注意力机制与可变卷积神经网络的卫星视频运动目标检测
Satellite Video Moving Object Detection Based on Deformable Convolutional Neural Network and Shuffle Attention
摘要
Abstract
Video satellites can obtain high spatial resolution video information,providing effective data support for the detection and analysis of moving targets.However,due to the disadvantages of low target pixel proportion,unclear texture details,and complex background in satellite video images,there are significant difficulties in detecting moving targets from satellite videos.Thus,on the basis of the backbone network of YOLOv8,this paper proposes a new detection method of motion targets in satellite video based on deformable convolutional neural network and Shuffle Attention.Firstly,a C2f-DCN module is designed to replace the C2f module in the original model backbone network for improving the model's ability of extracting features from targets with different types and different scales.Secondly,a lightweight Shuffle Attention mechanism is added in front of the detection head to strengthen important features while ensuring the computational speed of the model,enhancing information communication between channels,and improving the model's feature fusion ability.Finally,to improve the learning ability and inference efficiency of the model,the Inner-CIoU loss function is adopted,and the concept of auxiliary bounding boxes is introduced for solving the problem of small proportion of target pixels in satellite video images.Comparative experiments are conducted using the SAT-MTB satellite video image dataset,and the experimental results show that the accuracy,recall,mAP50:95,and F1 scores of the algorithm are 75.3％,62.8％,34.9％,and 68.48,respectively.Compared with the original YOLOv8n network,above indexes are improved by 11.6％,4.2％,3.0％,and 7.44.Thus,the effectiveness and superiority of the proposed method is verified.关键词
卫星视频/YOLOv8/轻量级注意力机制/可变形卷积/辅助边框回归Key words
satellite video/YOLOv8/lightweight attention mechanism/deformable convolution/auxiliary border regression分类
天文与地球科学引用本文复制引用
马洲俊,陈锦铭,刘浩林,张卡..基于注意力机制与可变卷积神经网络的卫星视频运动目标检测[J].南京师大学报（自然科学版）,2025,48(4):78-86,9.基金项目
国网江苏省电力有限公司科技项目(J2023121). （J2023121）
