在复杂的领域中,基于DETR的伪装的虫物体 key-fg
Dongmei Chen1, Peipei Cao1, Zhihua Diao2
1College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China.
Frontiers in plant science
|August 7, 2025
概括
本研究介绍了一种基于变压器的框架,用于检测伪装农业害虫. 我们的模型在复杂的环境中显著提高了害虫检测的准确性,有助于作物保护.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 伪装害虫对农业监测构成重大挑战,因为它们能够与复杂的背景混合.
- 精确的害虫检测对于有效的作物保护和产量优化至关重要.
研究的目的:
- 开发和评估一种基于变压器的新型检测框架,用于在实际农业环境中识别伪装的害虫.
- 在具有挑战性的环境条件下提高害虫检测系统的稳定性和准确性.
主要方法:
- 一个基于变压器的检测框架,包含一个细粒度得分预测器 (FGSP),MaskMLP用于实例感知面具,以及一个具有DropKey策略的Denoising模块.
- FGSP模块将对象查询引导到前景区域,而MaskMLP则生成像素级别的面具.
- 实施了Denoising模块和DropKey战略,以提高培训稳定性和注意力稳定性.
主要成果:
- 拟议的模型在COD10k数据集上获得了AP分数36.31,在虫数据集上达到75.07.
- 性能在COD10k上超过了可变形DETR的2.3%,在Locust上超过了3.1%.
- 在虫数据集上,回忆和F1得分分别有6.15%和6.52%的改善. 废弃性研究验证了单个模块的贡献.
结论:
- 开发的方法显著提高了复杂的农业环境中伪装害虫的检测.
- 该框架为农业害虫监测和作物保护应用提供了强大的解决方案.
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