一个改进的YOLO v4用于在非结构化的环境中检测葡萄
Canzhi Guo1, Shiwu Zheng1, Guanggui Cheng1,2
1Institute of Intelligent Flexible Mechatronics, Jiangsu University, Zhenjiang, China.
Frontiers in plant science
|July 31, 2023
概括
本研究介绍了YOLO v4+,这是一种用于收获机器人的增强视觉识别模型,可以在具有挑战性的非结构化环境中提高检测精度. 与原来的YOLO v4.4相比,该模型实现了更高的平均精度和F1分数.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视觉识别对于收集机器人的准确性至关重要.
- 非结构化的环境 (封闭,照明变化,雾) 挑战了当前的检测算法.
研究的目的:
- 提出一个改进的YOLO v4模型 (YOLO v4+),用于在非结构化环境中强大的果实检测.
- 为了增强特征提取和减少信息丢失,以获得更好的准确性.
主要方法:
- 在脊柱中实现了一个无参数的注意力机制.
- 引入了一个具有融合重量和跳跃连接的多尺度特征聚变模块.
- 使用的焦点损失与调整的超参数 (α=0.75, γ=2).
主要成果:
- YOLO v4+实现了94.25%的平均精度和93%的F1得分.
- 与YOLO v4.4相比,平均精度增加了3.35%,F1得分增加了3%.
- 在综合性和泛化能力方面表现优于其他最先进的模型.
结论:
- 拟议的YOLO v4+模型显著提高了收获机器人的检测准确性和稳定性.
- 定制增强方法在特定的工作条件下进一步提高模型性能.
- 该方法有可能提高机器人收获系统的适用性和稳定性.
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