基于Yolopose的挖掘机检测与工作状态识别

Excavator Detection and Working State Discrimination Based on Yolopose

针对光缆、高压油气管道等地下基础设施周边容易受到挖掘机的野蛮入侵问题.本文提出了一种结合Yolopose和多层感知机的挖掘机检测与工作状态判别方法.首先,设计了基于Yolopose的挖掘机6点姿势的提取网络Yolopose-ex;其次,利用Yolopose-ex模型提取视频中挖掘机工作姿态的变化信息,构建了挖掘机的工作状态特征向量(MSV);最后,利用深度学习算法多层感知机(multilayer perceptron,MLP)分析了视频中的挖掘机的工作状态.实验结果表明,所提出的方法克服了复杂背景难以识别的问题,对挖掘机工作状态识别准确率达到了96.6%,具有较高的推理速度和泛化能力.

The surrounding areas of underground infrastructure such as optical cables and high-pressure oil and gas pipelines are vulnerable to brutal invasion by excavators.This study proposes an excavator detection and working state discrimination method combined with Yolopose and a multilayer perceptron.First,the Yolopose-ex extraction network based on Yolopose’s six-point posture of the excavator is designed.Secondly,the Yolopose-ex model is utilized to extract the change information of the excavator’s working posture in the video,and the working state feature vector(MSV)of the excavator is constructed.Finally,the multilayer perceptron(MLP)is adopted to analyze the working status of the excavator in the video.The experimental results show that the proposed method overcomes the problem of difficult discrimination of complex backgrounds,and the accuracy of the identification of the working state of the excavator reaches 96.6%,which has a high reasoning speed and generalization ability.