YOLOv7-BW: 基于遥感图像的密集小目标高效检测器

作者

  • 葛 旭东 北京工商大学,计算机与人工智能学院,北京 100048 作者
  • 金 学波 北京工商大学,计算机与人工智能学院,北京 100048 作者
  • 马 慧鋆 北京工商大学,计算机与人工智能学院,北京 100048 作者
  • 邹 天畅 考克大学食品科学专业,考克,爱尔兰,T12 HY8E 作者

DOI:

https://doi.org/10.52810/JIR.2024.004

关键词:

遥感图像, YOLO, 目标检测, mAP

摘要

近年来,深度学习技术已经越来越广泛应用于遥感图像的检测。然而,遥感图像普遍目标大小差距大同时分布密集,对检测算法性能的要求高。目前的检测方法普遍效率低,容易出现漏检以及检测框不准确的情况。为此,本文基于YOLO算法进行改进,提出了一种基于YOLOv7的算法YOLOv7-bw,实现了对遥感图像的高效率检测,促进了目标检测在遥感行业的应用和发展。YOLOv7-bw在原始的池化金字塔SPPCSPC网络中添加了Bi-level Routing Attention模块,对目标集中区域重点关注,以提高网络提取特征的能力;并引入动态非单调的WIoUv3替换原本的CIoU损失函数,使得损失函数在每一时刻都能做出最符合当前情况的梯度增益分配策略,以提高对检测目标的聚焦能力。通过对DIOR遥感图像数据集进行对比实验发现,我们的YOLOv7-bw具有较高的mAP@0.5和mAP@0.5:0.95,在数据集上表现为85.63%和65.93%,高于YOLOv7源码的83.7%和63.9%分别1.93%、2.03%。同时,对比目前常用算法,我们的YOLOv7-bw均表现更好,证明了我们提出的算法是可行的,可以更好的应用于遥感图像检测。

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作者简历

  • 作者
    葛旭东 ,2024年毕业于北京工商大学控制工程专业,获硕士学位。研究方向为图像检测模式识别与信息融合、机器学习等。
  • 作者

    金学波 教授,博士生导师.1994 年毕业于吉林大学(原吉林工业大学)获学士学位,1997 年毕业于吉林大学(原吉林工业大学)获硕士学位,2004 年获得浙江大学控制科学与工程博士学位,导师为孙优贤院士. 研究方向为信息融合、模式识别与预测、大数据分析、深度学习等.近年来在相关领域主持了 1 项国家科技支撑计划课题、4 项国家自然基金面上项目等多项研究课题. 获 2021 年度中国粮油学会科学技术奖一等奖。在时序信号模式识别、图像目标检测与识别等研究领域,已发表 SCI、EI 收录等高水平学术论文 159 篇,其中 7 篇为 ESI 高被引论文(前 1%)、3 篇 ESI 热点论文(前 0.1%),已授权国家发明专利 20 余项,出版关于传感器信号识别与状态估计、多传感器信息融合的学术专著 3 部. 担任 SCI 收录期刊 Sensors 编委,为 IEEE/CAA Journal of Automatica Sinica、Knowledge-Based Systems 等中科院一区SCI 期刊审稿人.

  • 作者
    马慧鋆, 2010年毕业于长春光机学院原子与分子物理专业,获硕士学位,目前为北京工商大学系统科学专业在职博士生。研究方向为复杂系统建模、模式识别与信息融合、机器学习等。
  • 作者
    邹天畅,就读于爱尔兰考克大学食品科学专业。研究方向为食品感官评测和产品配方改造。

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YOLOv7-BW: 基于遥感图像的密集小目标高效检测器

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2024-05-30

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旭东葛., 学波金., 慧鋆马., & 天畅邹. (2024). YOLOv7-BW: 基于遥感图像的密集小目标高效检测器. 智能机器人, 1(1), 39-54. https://doi.org/10.52810/JIR.2024.004