计算机与数字工程2025，Vol.53Issue(1)：245-249,268,6.DOI:10.3969/j.issn.1672-9722.2025.01.044
基于改进yolov5的光学遥感图像风车目标检测研究
Research on Windmill Detection in Optical Remote Sensing Image Based on Improved yolov5
薛继伟 1吕福娟1
作者信息
- 1. 东北石油大学计算机与信息技术学院 大庆 163318
- 折叠
摘要
Abstract
The windmill detection in optical remote sensing image has a wide range of application prospects.Compared with natural scene images,satellite remote sensing image has the characteristics of large scale differences,smaller targets and complex backgrounds.Aiming at the problems of many interference false detection,low accuracy in complex backgrounds and low detection confidence in satellite image,a weighted bidirectional feature pyramid network(BiFPN)based on yolov5 is proposed,which intro-duces learnable weights to learn different input features.It realizes the two-way fusion of top-down and bottom-up deep and shallow features,enhances the transfer of feature information between different network layers,improves the target recognition rate in multi-scale and complex backgrounds,and improves the confidence of object detection.Finally,93.6%mAP,95.3%precision and 93.0%recall are obtained on the remote sensing windmill dataset.Compared with the original yolov5 network structure,the mAP is improved by 1.6%,which proves the effectiveness of the network improvement for target detection in the complex background of re-mote sensing images.关键词
光学遥感图像/风车/目标检测/BiFPN/特征融合Key words
optical remote sensing image/windmill/target detection/BiFPN/feature fusion分类
信息技术与安全科学引用本文复制引用
薛继伟,吕福娟..基于改进yolov5的光学遥感图像风车目标检测研究[J].计算机与数字工程,2025,53(1):245-249,268,6.
