电子器件2026，Vol.49Issue(1)：120-127,8.DOI:10.3969/j.issn.1005-9490.2026.01.018
基于改进YOLOv7的遥感图像目标检测算法
Target Detection Algorithm for Remote Sensing Images Based on Improved YOLOv7
摘要
Abstract
In response to the challenges posed by complex backgrounds,significant differences in target scales,and limited feature infor-mation for small targets in optical remote sensing images,an improved remote sensing target detection model based on YOLOv7 is pro-posed.The main improvements include the introduction of efficient multi-scale attention(EMA)in the backbone network to preserve multi-scale information in the feature map.The SPPCSPC module incorporates SPPF pooling structure and a large selective kernel at-tention mechanism(LSK).Additionally,the maximum pooling layer in the MP module is replaced by the space-to-depth layer(SPD)to reduce the loss of fine-grained feature information,thereby enhancing the capability to identify and locate small targets in complex back-grounds.The chosen loss function is the MPDIoU loss function,providing more accurate regression results and improving the model's convergence speed.Experimental results demonstrate that the proposed algorithm achieves an average precision mean average precision(mAP)of 95%on the NWPU VHR-10 dataset,representing a 3.2%improvement compared to the original model and effectively enhan-cing the detection accuracy of remote sensing targets.关键词
遥感图像/YOLOv7/小目标检测/注意力机制/损失函数Key words
remote sensing images/YOLOv7/small target detection/attention mechanisms/loss function分类
信息技术与安全科学引用本文复制引用
张瑞雪,陈琳..基于改进YOLOv7的遥感图像目标检测算法[J].电子器件,2026,49(1):120-127,8.基金项目
国家科技重大专项项目(2021DJ1006) （2021DJ1006）
湖北省科技基金项目(2019ZYYD016) （2019ZYYD016）
新疆自治区创新人才建设专项基金项目(2020D01A132) （2020D01A132）
