CAG-YOLO:轻量级网球检测
CAG-YOLO:Lightweight tennis detection
赵雨欣 1杨武 1李迎江 1卢玲1
作者信息
- 1. 重庆理工大学计算机科学与工程学院,重庆 400054
- 折叠
摘要
为实现智能网球回收机器人的高精度实时网球检测,提出一种轻量级网球检测算法CAG-YOLO.提出融合坐标注意力的Ghost残差块(coordinate attention ghostbottleneck,CAG),构建轻量型骨干网络CAG-Backbone,采用加权双向特征金字塔网络加强特征融合.采用SCYLLA-IoU计算坐标回归损失,改进非极大值抑制的后处理方法解决网球重叠问题.算法在Wtennis数据集上的实验结果表明,CAG-YOLO较基线方法的精度提高8.6％且模型体积减少31.7％,检测速度为21 ms,性能优于其它算法.CAG-YOLO能够用小规模参数提升检测精度,易于移植至移动智能设备.
Abstract
To realize the high-precision and real-time tennis detection of intelligent tennis recycling robot,a lightweight tennis detection algorithm CAG-YOLO was proposed.Coordinate attention ghostbottleneck(CAG)was proposed and a lightweight backbone network CAG-Backbone was constructed.Bidirectional feature pyramid network was used for feature fusion.SCYLLA-IoU was used to calculate the coordinate regression loss,and an improved post-processing method of non-maximum suppression was proposed to solve the problem of tennis overlap.Results of the experiment on Wtennis dataset show that the accuracy of CAG-YOLO is improved by 8.6％and the model volume is reduced by 31.7％compared with baseline method.The detection speed is 21 ms,outperforming that of the competitive algorithms.It verifies that CAG-YOLO can improve the detection accuracy with small-scale parameters and is easy to be transplanted to mobile intelligent device hardware.
关键词
目标检测/网球回收/深度学习/鬼影残差块/坐标注意力机制/双向特征金字塔/非极大值抑制Key words
object detection/tennis recycling/deep learning/ghostbottleneck/coordinate attention/bidirectional feature pyra-mid/non-maximum suppression引用本文复制引用
基金项目
国家社会科学基金(2017CG29)
重庆市教育科学规划课题(2021-GX-363)
重庆市研究生科研创新基金(CYS22661)
出版年
2024
NETL
NSTL
万方数据