为了实现大棚环境中番茄的智能在线产量估算,提出了一种基于改进的YOLOv5(You Only Look Once v5)番茄识别算法,对自然生长状态下的番茄果实产量进行统计和估算.首先,使用可分离视觉转换器(Separable Vision Transformer,SepViT)替换骨干网络的最后一层,以增强骨干网络与全局信息之间的联系并提取番茄特征;其次,引入WIOU(Wise Intersection over Union)损失函数和Mish激活函数,以提高收敛速度和精度.试验结果表明,改进后的检测模型在平均精度(mean Average Precision,mAP)方面达到了 99.5％,相较传统的YOLOv5模型提高了1.1个百分点,每张图像的处理时间为15ms.此外,改进后的YOLOv5算法对密集和遮挡情况下的番茄果实识别效果更好.
An improved YOLOv5-based method for tomato fruit identification and yield estimation
In pursuit of intelligent real-time yield estimation for tomatoes in greenhouse environments,we introduce an enhanced YOLOv5 tomato recognition algorithm aimed at the statistical assessment and estimation of tomato fruit yield in their natural growth conditions.Our approach involved two key enhancements:firstly,we substituted the final layer of the backbone network with a Separable Vision Transformer to augment the connectivity between the backbone network and global context,thereby facilitating tomato feature extraction;secondly,we incorporated the WIOU loss function and employ the Mish activation function to enhance convergence speed and accuracy.Experimental findings demonstrate that the improved detection model achieves a mAP score of 99.5％,reflecting 1.1 percentage points enhancement compared to the conventional YOLOv5 model,and the processing time for every image is 15ms.Furthermore,the improved YOLOv5 algorithm exhibits superior recognition performance for densely populated and occluded tomato fruits.
Greenhouse tomatoYOLOv5Attention mechanismLoss function
万方数据
维普
