YOLO-AVCA-CBAMNet:注意力驱动的框架,用于检测和分类绿胡的成熟阶段
Bipin Nair B J1, Abrav Nanda K M1, V Raghavendra1
1Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru Campus, India.
MethodsX
|January 26, 2026
概括
本研究介绍了YOLO-AVCA-CBAMNet,这是一个新的框架,可以使用注意力驱动的图像分析准确识别胡果的成熟度. 这种方法通过改善调味料生产的收获时间和质量控制来增强精密农业.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 准确的胡果成熟度识别对于最佳的收获时间和香料质量至关重要.
- 现有的方法可能会在不同的场景条件下扎,例如不同的照明和复杂的背景.
研究的目的:
- 开发一个综合检测和分类框架,YOLO-AVCA-CBAMNet,用于在自然野外环境中有效评估胡果的成熟度.
- 通过使用互补的注意力机制,提高到期阶段分离的可靠性.
主要方法:
- 在各种条件下利用自己收集的智能手机图像数据集对胡果的数据集.
- 在混乱的场景中使用YOLOv8进行初始果检测.
- 应用卷积神经网络增强了适应视觉皮层注意力模块 (AVCAM) 和卷积块注意力模块 (CBAM) 进行分类.
主要成果:
- 在不同的骨干架构中实现了5-9%的精度增长.
- 基于DenseNet121的配置达到96.19%的峰值精度.
- 通过双重注意力机制,通过视觉上类似的成熟阶段进行了改进的歧视.
结论:
- 注意驱动模型显示出在精密农业中具有可解释,高效和可扩展的成熟度评估的巨大潜力.
- YOLO-AVCA-CBAMNet框架为现实世界农业应用提供了强大的解决方案.
- 该研究证实了先进的深度学习技术在作物监测中的实际意义.
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