基于背景数据增强和改进YOLOv4的断路器试验机器人接线定位方法

Wiring locating method for circuit breaker test robot based on background augmentation and improved YOLOv4

为提高断路器试验机器人接线的准确性和可靠性,以双目视觉和深度学习目标检测技术为基础,提出一种基于背景数据增强和改进YOLOv4的断路器试验机器人接线定位方法。该方法利用本文提出的背景混合剪切的数据增强方法,解决因断路器训练图像背景特征不足而导致所训练的目标检测模型泛化能力和准确率低的问题,可以极大地提高不同试验场所(背景)和人员走动等背景扰动下机器人接线的准确性和可靠性;将标准YOLOv4的特征网络CSPDarknet-53替换为Mobiledets,可以优化目标检测模型的推理时间,提高机器人接线效率。仿真结果表明,本文方法的准确率为99.9%;实测结果表明,接线准确率为98.8%,全项目试验接线时间减少了57 s。通过对比分析,本文方法在接线准确率和时间上优于其他方法,可为断路器机器人试验平台的实用化提供技术支持。

In order to improve the accuracy and reliability of circuit breaker test robot wiring,a locating method with background augmentation and improved YOLOv4 on the basis of binocular vision and deep learning object detection technology is proposed in this paper.Background mixed shear method is adopted in the proposed method to solve the low generalization ability and accuracy problems caused by insufficient training background features.Therefore,the accuracy and reliability of wiring under the background disturbance such as different test sites and people walking are increased.Furthermore,the backbone of YOLOv4 is replaced to Mobiledets to optimize the reasoning period of the object detection model.So that the efficiency of robot wiring is improved.Simulation and test results show that the accuracy of detection model based on the proposed method is 99.9%,the robot wiring accuracy is 98.8%,and the wiring time is reduced by 57 s.Comparison and analysis indicate that,the method proposed in this paper is superior to other methods in robot wiring accuracy and time,which can provide technical support for the practicability of breaker robot test platform.