[1]彭芳,贺智明.信息收集和分发机制的遥感图像目标检测算法[J].计算机技术与发展,2025,(01):46-52.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0293]
　PENG Fang,HE Zhi-ming.Remote Sensing Image Object Detection Algorithm Based on Information Gather-and-distribute Mechanism[J].,2025,(01):46-52.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0293]
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信息收集和分发机制的遥感图像目标检测算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年01期
页码:
46-52
栏目:
媒体计算
出版日期:
2025-01-10

文章信息/Info

Title:
Remote Sensing Image Object Detection Algorithm Based on Information Gather-and-distribute Mechanism
文章编号:
1673-629X(2025)01-0046-07
作者:
彭芳; 贺智明
江西理工大学 信息工程学院,江西 赣州 341000
Author(s):
PENG Fang; HE Zhi-ming
School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China
关键词:
目标检测; 深度学习; 遥感图像; 信息收集和分发机制; NWD损失函数
Keywords:
object detection; deep learning; remote sensing image; information gather-and-distribute mechanism; normalized weighted distance loss function
分类号:
TP391.4
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0293
摘要:
遥感图像中的目标具有尺度多样、多类别以及背景复杂等特点,使得用于自然图像的目标检测算法在遥感图像目标检测中存在较多的漏检误检和精确度较低等现象。 针对这些问题,提出一种基于 YOLOv7 改进的信息收集和分发机制的遥感图像目标检测算法。 首先,设计一个信息收集和分发模块,用一个统一的模块收集不同特征图的信息并进行融合,将融合后的信息分发给需要的特征图,从而可以避免传统特征融合网络中固有的信息丢失,增强模型的特征融合能力从而提升模型的精确度。 其次,NWD 损失函数替换 CIoU 损失函数,缓解基于 IoU 的损失函数对小目标位置偏差的敏感,减少目标的漏检和误检。 为了提升模型的收敛速度,设计一个超参数,同时使用 NWD 损失函数和 IoU 损失函数。 在公开数据集 RSOD 上进行的实验结果表明,在复杂多样的情况下,改进后算法的平均精度均值( mAP) 达到 96. 5% ,相较原 YOLOv7 算法提升了 15. 8 百分点,证明了改进后算法的适用性和有效性。
Abstract:
The targets in remote sensing images have the characteristics of diverse scales,multiple categories,and complex backgrounds,which makes the target detection algorithms used for natural images prone to missed detections,false detections,and low accuracy in remote sensing image target detection. A remote sensing image object detection algorithm based on YOLOv7 improved information gather-and-distribute mechanism is proposed to address these issues. Firstly,we design an information gather-and-distribute mechanism that uses a unified module to collect and fuse information from different feature maps,and distribute the fused information to the required feature maps. This can avoid the inherent information loss in traditional feature fusion networks,enhance the feature fusion ability of the model,and improve its accuracy. Secondly,the NWD loss function replaces the CIoU loss function to alleviate the sensitivity of IoU based loss functions to small target position deviations,reducing missed and false detections of targets. To improve the convergence speed of the model,we design a hyperparameter and use both NWD loss function and IoU loss function. The experimental results conducted on the public dataset RSOD show that in complex and diverse situations,the average precision mean (mAP) of the improved algorithm reaches 96. 5% , which is 15. 8 percentage points higher than that of the original YOLOv7 algorithm, proving the applicability and effectiveness of the improved algorithm.

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更新日期/Last Update: 2025-01-10