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基于STDA-YOLOv8的小目标检测算法

Cun Li¹, Shuhai Jiang¹, Xunan Cao¹

¹School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|May 14, 2025

概括

这项研究介绍了STDA-YOLOv8,这是一种用于小目标检测的增强算法. 它在VisDrone上提高了5.3%,在PASCAL VOC上提高了5.7%,克服了数据不平衡和检测限制.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

由于网络限制和不平衡的训练数据,小目标检测具有挑战性,导致错误阳性和错过检测.
现有的数据集往往缺乏足够的小物体注释,阻碍了模型的性能.

研究的目的:

提出一种新的算法STDA-YOLOv8,以提高小目标检测能力.
解决特征提取和数据不平衡方面的局限性,以改善小物体识别.

主要方法:

设计了一种新的网络架构,包括具有多尺度扩展卷积的上下文增强模块 (CAM) 和用于自适应特征融合的特征改进模块 (FRM).
引入了复制-减少-粘贴数据增强技术,以减轻小型和大型对象之间的注释差异.
在VisDrone和PASCAL VOC数据集上进行了除和比较实验.

主要成果:

在VisDrone数据集上,STDA-YOLOv8实现了93.5%的准确性,比YOLOv8.8提高了5.3%.
在PASCAL VOC数据集上实现了94.2%的准确性,比YOLOv8.7有5.7%的改进.
超越了主流目标检测模型和专门的小目标检测算法,如QueryDet.

结论:

拟议的STDA-YOLOv8通过改善特征提取和解决数据不平衡,有效地提高了小目标检测性能.
新型CAM和FRM模块显著提高了小目标的检测精度.
复制-减少-粘贴增强方法在处理注释差异方面被证明是有效的,有助于整体模型的稳定性.

关键词:

这就是YOLOv8的意义.语境增强是指上下文增强.功能提炼功能提炼功能提炼小目标检测检测小目标检测

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