摘要
为了解决复杂场景下激光跟踪仪对合作目标靶球的精确识别难题,提出了基于深度学习的合作目标靶球高效检测方法。首先分析了合作目标靶球的图像特征,然后采用改进的YOLOv2模型,针对合作目标靶球多尺度与小目标占比多的特点,提出了一种基于注意力机制的改进方法,同时为提高网络模型对复杂背景的抗干扰能力,提出了一种数据增强方法。测试结果表明,所提出的基于注意力机制与数据增强的改进YOLOv2模型对复杂背景的抗干扰能力较强,且对合作目标靶球的检测精度有显著提高,在合作目标靶球测试集上的检测准确率达到92.25%,能够有效满足激光跟踪仪在大型装置精密装配过程中的目标检测精度需求。
In order to improve the detection accuracy of cooperative target ball used for the precision assembly of large-scale devices by laser tracker in complex scenes,an efficient cooperative target ball detection method based on deep learning is researched.Firstly,the image features of the cooperative target are analyzed.Then,by using the improved YOLOv2 model,an improved method based on attention mechanism is proposed aiming to the cooperative target characteristics of multi-scale and large proportion of small targets.In order to improve the anti-interference ability of the network model in complex background,a method of data enhancement is also proposed.The test result shows that the proposed improved YOLOv2 network based on attention mechanism and data enhancement has strong anti-interference ability against complex background and significantly improves the detection accuracy of cooperative target ball.The detection accuracy on the cooperative target test set has reached 92.25%,which meets the target detection accuracy requirements of laser tracker in the large equipment precision assembly.
作者
王国名
郝灿
石俊凯
高超
王博
周维虎
高豆豆
WANG Guoming;HAO Can;SHI Junkai;GAO Chao;WANG Bo;ZHOU Weihu;GAO Doudou(Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 101408,China)
出处
《计测技术》
2022年第3期16-22,共7页
Metrology & Measurement Technology
基金
国家重点研发计划(2019YFB2006100)。
