对农场复杂元素的改进实例细分方法 无人机空中调查图像 无人机空中调查图像
Feixiang Lv1, Taihong Zhang1,2,3, Yunjie Zhao1,2,3
1School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究介绍了一种改进的SparseInst算法,用于高精度的农场空中图像细分. 改进的模型准确地识别了复杂的农场目标,以更少的参数优于现有的方法.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 传统的农场层建筑是低效和昂贵的.
- 精确的农场元素细分对于无人操作至关重要,例如路径规划和预警.
研究的目的:
- 开发一个高精度的实例细分算法,用于农场空中图像.
- 提高创建农场调查层的效率,降低创建农场调查层的成本.
主要方法:
- 提出了一种基于SparseInst的算法,该算法包含一个具有心脏卷积的多尺度注意模块 (MSA).
- 将一个自下而上的聚合路径集成到特征金字塔融合网络中.
- 在模型的部内嵌入了协调注意力块 (CA),以增强语义理解.
主要成果:
- 与Mask R-CNN和Cascade-Mask相比,改进的SparseInst模型在复杂的农场目标上实现了更高的细分精度.
- 与SOLOv2和Condinst相比,显示的准确性分别提高了10.8%和12.8%.
- 该模型需要比较方法中最小数量的参数,并支持实时细分.
结论:
- 拟议的算法有效地对农场空中图像中的多样化和复杂的目标进行细分.
- 这种方法为生成农场调查层提供了具有成本效益和准确性的解决方案.
- 该模型适用于在具有挑战性的农场环境中的实时应用.
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