在智能温室中优化番茄检测和计数:一个轻量级的YOLOv8模型,包含高频和低频特征变压器结构
Zhimin Tian1, Huijuan Hao1, Guowei Dai2,3,4
1Hebei University of Water Resources and Electric Engineering, Cangzhou, Hebei, China.
Network (Bristol, England)
|November 21, 2024
概括
这项研究介绍了TCAttn-YOLOv8,这是一种用于在智能温室中检测和计数西红的先进算法. 它显著提高了机器人收获和产量估计的准确性和效率.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 智能温室需要自动化解决方案,以实现高效的番茄收获.
- 机器人收获需要先进的视觉感知系统来准确检测和计数.
- 现有的算法在复杂的温室环境中与小型和封闭的番茄目标作斗争.
研究的目的:
- 为智能温室开发一个改进的番茄检测和计数算法.
- 为了提高YOLOv8的性能,用于识别小物体和封闭物体.
- 为自动化番茄收获和产量估计提供强大的解决方案.
主要方法:
- 拟议的TCAttn-YOLOv8算法集成了一个新的检测层 (NDL),ColBlock,C2fGhost和GhostConv.
- 使用了Wise-IoU (WIoU) 损失函数,以加快融合和提高回归精度.
- 实现了变压器优势,用于在复杂的背景中增强特征提取和融合.
主要成果:
- TCAttn-YOLOv8实现了96.31%的平均平均精度 (mAP@0.5) 和每秒95 (FPS).
- 该算法展示了270万的参数大小,超过了其他七个轻量级YOLO算法.
- 实现了0.9282的自动番茄计数相关系数 (R),验证了其适用于取代手动方法的适用性.
结论:
- TCAttn-YOLOv8有效地支持智能温室中的番茄检测和计数.
- 开发的算法为机器人收获和产量估计研究提供了宝贵的见解.
- 这种方法增强了对农业自动化至关重要的视觉感知能力.
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