基于YOLOv5-SPD改进的杂草识别算法
Improved weed identification algorithm based on YOLOv5-SPD
羊智凡 1李海波1
作者信息
- 1. 上海工程技术大学 电子电气工程学院, 上海 201620
- 折叠
摘要
杂草的精确识别是实现机器代替人工除草的首要前提.初生的杂草目标小,识别难度大.YOLOv5-SPD在小目标识别上有着良好的表现,但在稳健性及准确性上还有待提高.在YOLOv5-SPD基础上加入通道注意力机制可以加强有效特征的权重值,使网络的学习更具有针对性.同时将广义交并比(GIoU)损失函数替换成完全交并比(CIoU)损失函数,可有效解决边框重合关系问题和目标框与预测框的高宽比以及中心点之间的关系,使杂草预测框更加接近真实框.杂草数据集上的试验结果表明,改进后的网络检测精度达到 70.3%,准确率达到 94.1%,比原来的YOLOv5-SPD分别提高4.7%和2.8%.
Abstract
Accurate identification of weeds is the primary prerequisite for achieving machine replacement of manual weeding.The target of nascent weeds is small,making identification difficult.YOLOv5-SPD has good performance in small target recognition,but its robustness and accuracy still need to be improved.Adding channel attention mechanism on the basis of YOLOv5-SPD can strengthen the weight value of effective features,making the learning of the network more targeted.At the same time,replacing the generalized intersection over union(GIoU)loss function with complete intersection over union(CIoU)can effectively solve the problem of border coincidence,the height width ratio of the target box and the prediction box,and the relationship between the center point,there by making the weed prediction box closer to the real box.The experimental results on the weed dataset show that the improved network detection accuracy reaches 70.3%with an accuracy rate of 94.1%which is 4.7%and 2.8%higher than the original YOLOv5-SPD.
关键词
注意力机制/边界损失函数/YOLOv5-SPD算法/杂草识别Key words
attention mechanism/boundary loss function/YOLOv5-SPD algorithm/weed identification引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据