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%。
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.
作者
羊智凡
李海波
YANG Zhifan;LI Haibo(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《上海工程技术大学学报》
CAS
2024年第1期75-82,共8页
Journal of Shanghai University of Engineering Science
