基于CPDD-YOLOv8的空中图像中的小型物体检测模型
Jingyang Wang1, Jiayao Gao1, Bo Zhang2
1School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
Scientific reports
|January 4, 2025
概括
本研究介绍了CPDD-YOLOv8,这是一个用于在空中图像中检测小物体的增强模型. CPDD-YOLOv8显著提高了检测准确度和速度,在基准数据集上表现优于现有的模型.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 空中图像包含许多小物体,由于微妙的特征和背景干扰,对准确的检测提出了挑战.
- 现有的物体检测模型在高海拔图像中扎着小物体的尺度变化和低可见性.
研究的目的:
- 开发一个改进的物体检测模型,CPDD-YOLOv8,专门用于增强空中图像中小物体的检测.
- 整合新的架构组件,以提高模型捕获语义信息和适应各种视觉输入的能力.
主要方法:
- 拟议的CPDD-YOLOv8结合了C2fGAM以增强语义理解,用于浅特征提取的P2检测层,以及用于自适应处理的DSC2f与动态蛇卷积.
- 该模型采用动态头 (DyHead) 与集成的注意力机制,以优化特征层权重.
- 在VisDrone2019数据集上进行了实验,包括剥离研究和与其他七种模型进行比较.
主要成果:
- 废弃实验证实了CPDD-YOLOv8.8中所有拟议模块的有效性.
- 与其他七种模型相比,CPDD-YOLOv8的平均精度 (mAP) 更高,mAP@0.5达到41% (比YOLOv8提高6.9%).
- 小物体检测率有13.1%的改进,模型的概括性在WiderPerson,VOC_MASK和SHWD数据集上得到了验证.
结论:
- 与现有的方法相比,CPDD-YOLOv8在从空中图像中检测小物体方面表现出卓越的性能.
- 新的建筑增强有效地解决了在复杂的空中场景中检测小的,功能较差的物体的挑战.
- 该模型的强大性能和通用性表明其在空中监视和分析中具有实际应用的潜力.
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