基于视觉跟踪和模式识别的缆机吊运效率分析  

Productivity analysis of cable crane transportation based on visual tracking and pattern recognition

作　　者：王浩[1] 杨启贵 刘全[1,3] 赵春菊 张宏阳 WANG Hao;YANG Qigui;LIU Quan;ZHAO Chunju;ZHANG Hongyang(Institute of Water Engineering Sciences,Wuhan University,Wuhan 430072,China;CISPDR Corporation,Wuhan 430010,China;State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China;School of Civil Engineering Architecture and Environment,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]武汉大学水工程科学研究院,武汉430072 [2]长江设计集团有限公司,武汉430010 [3]武汉大学水资源工程与调度全国重点实验室,武汉430072 [4]湖北工业大学土木建筑与环境学院,武汉430068

出　　处：《清华大学学报（自然科学版）》2024年第9期1646-1657,共12页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金面上项目(51779131)。

摘　　要：缆机是拱坝施工中主要的混凝土入仓设备。监测缆机吊运过程并分析其施工效率,对于支撑工程现场调度优化、资源配置和指导施工至关重要。该文提出一种基于视觉跟踪和模式识别的缆机吊运混凝土效率智能分析方法,首先训练YOLO模型作为目标检测器,并引入轨迹片段特征改进DeepSORT算法,实现对缆机吊罐移动轨迹的多目标跟踪;然后基于轨迹数据的时序特征建立缆机吊运混凝土工作状态识别模型,实现从海量轨迹数据中快速准确识别缆机工作状态和计算吊运效率。拱坝工程的实例分析结果表明:该方法实现了高精度完整跟踪吊罐的移动轨迹,MOTA指标和IDF1指标分别高达90.0%和94.8%,准确识别出了缆机的6种工作状态,精准计算出了缆机的浇筑强度等效率指标,具备可靠性和准确性。[Objective]Cable cranes are the main concrete transportation equipment used in arch dam construction.Productivity analysis of cable crane transportation is crucial for improving scheduling management,reducing operational costs,and controlling dam construction progress.However,the traditional manual recording method for analyzing cable crane productivity is time-consuming and labor-intensive.Moreover,existing monitoring methods,such as sensors and global navigation satellite systems,are susceptible to interference because of the challenging environment and complicated operating space at dam construction sites.Furthermore,they usually entail high installation and maintenance costs.Therefore,this study proposes an intelligent monitoring method based on visual tracking and pattern recognition for cable crane transportation in dam construction.[Methods] The proposed method initially tracks the process of cable crane transporting concrete using visual tracking technology to obtain the complete moving trajectory of crane buckets.Subsequently,it establishes a pattern recognition model to automatically identify the working states of cable cranes and calculate their productivity by analyzing the time-series features of the trajectory data.In the visual tracking of cable cranes,the main challenge is to address the similar appearance and occlusion problems of crane buckets.Therefore,we propose a new multiobject tracking framework by introducing a rematching mechanism based on tracklet features(segments of the entire object trajectory),which effectively reduces the occurrences of ID switches and enhances tracking accuracy.Additionally,You Only Look Once(YOLO)model is trained as the object detector of the tracking framework.Subsequently,trajectory data obtained by visual tracking is used as input for the pattern recognition model of cable crane working states,with the output being the pouring productivity.This pattern recognition model employs spline interpolation and Savitzky-Golay filters to solve the problems of missing val

关 键 词：水利工程施工 缆机 多目标跟踪 模式识别 目标识别 

分 类 号：TV522[水利工程—水利水电工程]