SS-OPDet:一个半监督的开放式检测框架,用于检测枯木松木.
Xiaojian Lu1, Shiguo Huang1, Songqing Wu2
1College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
检测死松树木的松病对森林管理至关重要. 一个新的半监督开放集检测框架 (SS-OPDet) 通过使用未标记的数据来提高准确性,减少错误和注释需求.
科学领域:
- 林业和环境科学 林业和环境科学
- 计算机视觉和机器学习
- 植物病理学 植物病理学
背景情况:
- 松病是松树林的全球威胁,需要有效检测受影响的树木.
- 传统的监督方法与未知对象作斗争,导致假阳性和高注释成本.
- 精确检测枯的松木对于疾病控制和森林管理至关重要.
研究的目的:
- 开发一种高效准确的半监督开放式检测框架,用于识别死松木.
- 克服传统方法在处理未知干扰对象方面的局限性,并减少注释负担.
- 为了改善松病的检测,以便更好地监测森林.
主要方法:
- 建议SS-OPDet,一个半监督的开放集检测框架,利用标记和未标记的数据.
- 集成了一个加权的多尺度特征融合模块,用于增强特征表示.
- 实施了动态信心伪标签生成策略,以减少噪音和利用未标签的数据.
主要成果:
- 在7733张无人机图像上,SS-OPDet实现了84.73%的平均精度 (APK) 和94.48%的回忆 (RK).
- 证明低的绝对开放式设置误差 (AOSE) 为271%,野生环境影响 (WI) 为0.0917%.
- 跨区域验证证实了该框架的稳定性和通用化能力.
结论:
- 该SS-OPDet框架提供了一个具有成本效益和准确的解决方案,用于检测死松木.
- 这种方法显著改善了及时检测松病的情况.
- 为森林监测和管理策略提供了巨大的好处.
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