[1]刘俊苗,王海晨.基于改进YOLOv5s的遥感图像车辆目标检测[J].计算机技术与发展,2024,34(12):87-92.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0283]
　LIU Jun-miao,WANG Hai-chen.Vehicle Target Detection in Remote Sensing Images Based on Improved YOLOv5s[J].,2024,34(12):87-92.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0283]
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基于改进YOLOv5s的遥感图像车辆目标检测()

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

卷:
34
期数:
2024年12期
页码:
87-92
栏目:
人工智能
出版日期:
2024-12-10

文章信息/Info

Title:
Vehicle Target Detection in Remote Sensing Images Based on Improved YOLOv5s
文章编号:
1673-629X(2024)12-0087-06
作者:
刘俊苗; 王海晨
长安大学 信息工程学院,陕西 西安 710064
Author(s):
LIU Jun-miao; WANG Hai-chen
School of Information Engineering,Chang’an University,Xi’an 710064,China
关键词:
遥感图像; 车辆目标检测; YOLOv5s; 自注意力机制; 轻量化上采样算子; 损失函数
Keywords:
remote sensing images;  vehicle target detection;  YOLOv5s;  self - attention mechanism;  lightweight upsampling operator; loss function
分类号:
TP751;U495
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0283
摘要:
遥感影像在目标检测领域被广泛应用,在车辆目标检测方面,由于遥感图像自身背景复杂、车辆目标小且密集等特点,现有目标检测方法常面临检测效果不佳以及漏检误检等问题。 该文针对性地提出了一种改进 YOLOv5s 的目标检测方法。 引入了带有位置信息编码的自注意力机制,强化对同一尺度内特征信息的融合,在网络高层采用 MobileViT 处理更抽象的特征,高效捕捉复杂的上下文信息,进一步增强了对小目标的识别能力,解决了小目标漏检问题;应用轻量级上采样算子来降低模型复杂度;另外,应用 ShapeIoU 损失函数,使该模型更加关注边界框的形状和尺度以便提高检测精度。 在遥感车辆数据集 COWC 进行了完整实验,实验表明改进后的模型各项指标均有提升,准确率提高了 0. 3 百分点,召回率提高了 1 百分点,mAP 提高了 0. 8 百分点。
Abstract:
Remote sensing images are widely used in the field of target detection. In the vehicle target detection,due to the complex back-ground of remote sensing images themselves and the small and dense vehicle target,the existing target detection methods often face the problems of poor detection effect,missing detection and misdetection. In view of this,we propose an improved YOLOv5s target detection method. A self-attention mechanism with position information encoding is introduced to strengthen the fusion of feature information within the same scale. MobileViT is employed at the higher level of the network to handle more abstract features and capture complex contextual information efficiently,which further enhances the recognition ability of small targets and solves the problem of small target miss-detection. A lightweight up-sampling operator is applied to reduce the model complexity. In addition,the ShapeIoU loss function is applied to make the model pay more attention to the shape and scale of the boundary frame in order to improve the detection accuracy.A complete experiment is conducted on the remote sensing vehicle data set - COWC. It is showed that all indicators in the model have been improved,with the accuracy increased by 0. 3 percentage points,the recall increased by 1 percentage point,and the mAP increased by 0. 8 percentage points.

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更新日期/Last Update: 2024-12-10