摘要
由于航拍图像中的目标存在遮挡、重叠等问题,导致模型难以稳定识别,降低了军事目标追踪、交通监管、灾害观察等领域的工作效率。为解决上述问题,提出了一种基于改进YOLOv5的航拍图像检测方法。该方法引入用于低分辨率图像和小物体的新卷积神经网络模块(Space-to-depth Convolution,SPD-Conv)、小目标检测头、软非极大值抑制算法(Soft Non-maximum Suppression,Soft-NMS)和回归损失函数,并在VisDrone2019数据集上进行了大量实验。实验结果表明:所提方法在VisDrone2019数据集上平均准确率提高12.5%,mAP@0.5:0.95指标提高9.3%。
Due to issues such as occlusion and overlapping in aerial images,it is challenging for models to achieve stable recogni⁃tion,which reduces efficiency in areas such as military target tracking,traffic monitoring,and disaster observation.To address these problems,a method based on improved YOLOv5 for aerial image detection has been proposed.This method introduces a new convolu⁃tional neural network module(Space-to-depth Convolution,SPD-Conv)for low-resolution images and small objects,a small object de⁃tection head,a soft non-maximum suppression algorithm(Soft Non-maximum Suppression,Soft-NMS),and a regression loss function.Extensive experiments have been conducted on the VisDrone2019 dataset.The experimental results show that the proposed method achieves an average accuracy improvement of 12.5%and a 9.3%increase in mAP@0.5:0.95 metric on the VisDrone2019 dataset.
作者
王嘉锵
刘子德
王绪娜
高宏伟
WANG Jiaqiang;LIU Zide;WANG Xu′na;GAO Hongwei(Shenyang Ligong University,Shenyang 110159,China)
出处
《通信与信息技术》
2024年第1期29-33,共5页
Communication & Information Technology
基金
辽宁省重点科技创新基地联合开放基金,基于机器视觉的空间站机械臂定位技术研究(2021-KF-12-05)。
