ARSOD-YOLO:增强用于远程传感图像的小目标检测
Yijuan Qiu1,2,3, Xiangyue Zheng1,2,3, Xuying Hao1,2,3
1National Laboratory on Adaptive Optics, Chengdu 610209, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究介绍了ARSOD-YOLO,这是一个增强的YOLOv8模型,用于改进远程传感图像中的小目标检测. 它通过先进的特征提取和融合技术实现了卓越的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感技术 遥感技术 遥感技术
- 人工智能的人工智能
背景情况:
- 遥感图像对于环境监测,农业和自动驾驶至关重要.
- 在这些图像中检测小目标具有重大挑战,影响各种应用.
- 现有的方法需要在特征提取,融合和模型优化方面进行改进.
研究的目的:
- 开发一个先进的物体检测模型,以改善远程传感图像中的小目标识别.
- 增强特征提取和融合机制,以提高检测准确度.
- 优化整体网络架构和损失函数以提高效率和性能.
主要方法:
- 引入了适应性选择性特征增强模块 (AFEM) 进行动态特征重量调整.
- 开发了适应式多尺度卷积内核特征融合模块 (AKSFFM),用于增强特征融合.
- 优化了YOLOv8架构,结合了新的组件模块和损失函数,从而产生了ARSOD-YOLO模型.
主要成果:
- 在基准数据集 (VEDAI和AI-TOD) 上,ARSOD-YOLO表现出卓越的性能.
- 在VEDAI数据集上实现了74.3%的mAP50,比YOLOv8基线提高了3.1%.
- 在AI-TOD数据集上达到47.8%的mAP50,超过基线6.1%.
结论:
- 拟议的ARSOD-YOLO模型显著提高了远程传感图像中小型目标检测能力.
- 创新的AFEM和AKSFFM模块有助于改进特征表示和融合.
- 在关键的遥感应用中,ARSOD-YOLO为准确和高效的对象检测提供了一个有前途的解决方案.
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