基于改善的YOLOv5s在自然环境中的西红的准确和快速检测
Philippe Lyonel Touko Mbouembe1, Guoxu Liu2,3, Sungkyung Park1
1Department of Electronics Engineering, Pusan National University, Busan, Republic of Korea.
Frontiers in plant science
|January 26, 2024
概括
这项研究引入了SBCS-YOLOv5s,这是一个改进的算法,用于在自然环境中检测番茄,达到87.7%的平均精度. 增强型号在具有挑战性的条件下脱而出,例如不均的照明和水果重叠,优于标准方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 农业技术 农业技术
背景情况:
- 自动收获机器人因不均的照明,遮蔽和水果重叠而难以检测番茄.
- 现有的检测算法往往缺乏对现实世界农业环境所需的稳定性.
研究的目的:
- 为在自然环境中运行的自动化收获机器人开发一个高度准确和熟练的番茄检测算法.
- 为了提高YOLOv5s模型的特征表达和强度,以改进番茄识别.
主要方法:
- 拟议的SBCS-YOLOv5s算法将SE关注,双向特征金字塔网络 (BiFPN),内容意识重组特征 (CARAFE) 和软非最大抑制 (Soft-NMS) 模块集成到YOLOv5s架构中.
- 关键的修改包括将C3模块替换为C3SE,将PANet替换为BiFPN,使用CARAFE进行增量采样,并使用Soft-NMS进行阻塞处理.
主要成果:
- SBCS-YOLOv5s的平均精度 (mAP) 达到87.7%,比原始YOLOv5s模型提高3.5%.
- 该算法显示每个图像的检测速度为2.6ms,超过了其他最先进的检测算法.
- 集成模块增强了特征提取,提高了对环境变化的稳定性,更好地处理重叠和封闭的西红.
结论:
- 在农业机器人领域,SBCS-YOLOv5s在番茄检测准确性和效率方面取得了重大进展.
- 该算法对自动收获的实际应用非常有前途,特别是在复杂的自然环境中.
- SE,BiFPN,CARAFE和Soft-NMS的组合有效地解决了在遮蔽和重叠下检测番茄的挑战.
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