在基于圆形边界盒和大型选择性内核的遥感图像中检测储
Yu Liu1, Yong Wan2, Weimin Huang3
1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China.
Scientific reports
|December 18, 2025
概括
本研究引入了一种使用圆形界限盒和大型选择性内核 (LSK) 的新方法,用于在遥感图像中准确检测储,从而改善石油和天然气行业的甲排放监测.
科学领域:
- 环境监测环境监测环境监测
- 遥感技术是远程传感技术.
- 温室气体检测检测的温室气体检测
背景情况:
- 石油和天然气产生的甲排放量很大.
- 现有的储检测方法存在局限性.
- 深度学习模型与小的,多尺度的对象和背景干扰作斗争.
研究的目的:
- 开发一种改进的方法,用于在遥感图像中检测储.
- 为了提高甲排放源识别的准确性.
- 支持石油和天然气行业的环境可持续性.
主要方法:
- 整合圆形界限框,为稳定的交叉路口在欧盟 (IoU).
- 实施大型选择性内核 (LSK) 进行动态受感场调整.
- 使用具有全面数据集的YOLO-v10框架.
主要成果:
- 实现了0.911的精度,0.902的召回,以及0.931的mAP@0.5
- 与YOLO-v10基线相比,精度有2.0%,回忆有2.7%,mAP@0.5有1.8%的改善.
- 成功应对小物体,多尺度特征和背景干扰的挑战.
结论:
- 这种新方法为储检测提供了一个强大的解决方案.
- 提高了确定甲排放源的准确性.
- 该方法有助于环境监测和可持续发展努力.
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