CAM-YOLO:基于改进的YOLOv5的西红检测和分类,使用结合注意力机制
Seetharam Nagesh Appe1,2, Arulselvi G1, Balaji Gn3
1Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India.
PeerJ. Computer science
|August 7, 2023
概括
一个改进的YOLOv5物体检测模型CAM-YOLO,准确地识别番茄成熟阶段. 这种自动化方法提高了番茄生产的质量控制,提高了利能力.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 番茄的质量对于一致的营销至关重要,成熟度是消费者驱动的关键因素.
- 自动成熟度评估对于优化番茄生产和提高利能力至关重要.
- 目前的物体检测方法与复杂的背景,纹理变化和番茄果识别中的遮蔽作斗争.
研究的目的:
- 为番茄成熟度测试提出一个自动化的多类分类方法.
- 开发一个改进的物体检测算法,用于准确的番茄识别.
- 提高番茄生产中成熟度评估的效率和准确性.
主要方法:
- 这项研究提出了一种改进的YOLOv5算法,称为CAM-YOLO,用于番茄检测.
- CAM-YOLO将YOLOv5与卷积区注意模块 (CBAM) 集成,用于特征提取和目标识别.
- 诸如非最大压制和距离与工会交叉 (DIoU) 等技术被用于改进对重叠物体的检测.
主要成果:
- CAM-YOLO算法在检测重叠和小番茄方面表现出了高效率.
- 拟议的模型在番茄检测中实现了88.1%的平均精度.
- 在各种条件下,实验结果证实了CAM-YOLO与标准YOLOv5相比的优越性能.
结论:
- CAM-YOLO为番茄成熟度分类提供了一种有效的自动化解决方案.
- 改进的物体检测算法提高了识别番茄的准确性,特别是在具有挑战性的场景中.
- 这项技术有可能通过确保高质量的产品产量和提高利能力,使番茄产业显著受益.
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