■茶叶采摘点的识别和定位是实现名茶自动采摘的前提。然而,由于茶芽与幼叶和老叶之间的颜色相似,人眼很难准确识别它们。
■为了解决分割问题,检测,在机械采摘名茶的复杂环境中定位采茶点,本文提出了一种称为MDY7-3PTB模型的新模型,结合了DeepLabv3+的高精度分割能力和YOLOv7的快速检测能力。该模型首先实现了分割的过程,其次是茶芽的检测和最终的定位,从而准确识别茶芽采摘点。该模型用更轻量级的MobileNetV2网络代替DeepLabv3+特征提取网络,以提高模型计算速度。此外,多注意机制(CBAM)融合到特征提取和ASPP模块中,以进一步优化模型性能。此外,为了解决数据集中的类不平衡问题,焦点损失函数用于纠正数据不平衡和改善分割,检测,和定位精度。
■MDY7-3PTB模型实现了86.61%的联合平均交点(mIoU),平均像素精度(mPA)为93.01%,茶芽分割数据集上的平均召回率(mRecall)为91.78%,比PSPNet等通常的分割模型表现更好,Unet,和DeeplabV3+。在茶芽采摘点识别与定位方面,该模型实现了93.52%的平均精度(mAP),准确率和召回率的加权平均值(F1得分)为93.17%,精度为97.27%,召回率达到89.41%。与现有的主流YOLO系列检测模型相比,该模型在各方面都有显著的改进,具有很强的通用性和鲁棒性。该方法消除了背景的影响,直接检测茶芽采摘点,几乎没有漏检,为茶芽采摘点提供精确的二维坐标,定位精度达96.41%。这为今后的茶芽采摘提供了有力的理论依据。
UNASSIGNED: The identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.
UNASSIGNED: To address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.
UNASSIGNED: The MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking.