工程机械的深度学习和计算机视觉算法相关论文简介

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0.State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

• 发表于Tunnelling and Underground Space Technology/Volume 113, July 2021
• 期刊2022年影响因子/JCR分区：6.407/Q1
• This paper aims at reviewing the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling.
• 本文旨在综述人工智能技术在地下土-结构相互作用研究中的应用，重点从土与岩石表征、桩基、深基坑和隧道等方面进行研究。
• An overview of different AI techniques is provided and a list of key AI applications in underground works that have been published in the last ten years is also compiled to study the recent trend of machine learning techniques in underground construction.
• 本文概述了不同的人工智能技术，并列出了近十年来在地下工程中发表的主要人工智能应用列表，以研究机器学习技术在地下建设中的最新趋势。

1.Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning

Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning

• 发表于JOURNAL OF COMPUTING IN CIVIL ENGINEERING/Volume 32 Issue 2 - March 2018
• 期刊2022年影响因子/JCR分区：5.802/Q1
• 这篇文章提出了一种基于深度卷积网络的施工目标检测方法，实现了对施工设备的准确识别。
• This paper proposes a deep convolutional network-based construction object-detection method to accurately recognize construction equipment
• 深度卷积网络可以在各种视觉任务中取得较高的性能，但在缺乏足够公开可用数据进行训练的建筑行业中不容易应用。这一问题可以通过迁移学习来解决，它通过转移在其他领域训练过的模型的知识，用大量的训练数据来训练建筑业的模型。
• A deep convolutional network can achieve high performance in various visual tasks, but is not easy to be applied in the construction industry where there is not enough publicly available data for training. This problem is solved by transfer learning, which trains a model for the construction industry by transferring the knowledge of models trained in other domains with a large amount of training data.
• 在未来，提出的模型可以用来推断建设操作的环境，以产生管理信息，如进度、生产力和安全。
• In the future, the proposed model can be used to infer the context of construction operations for producing mana-gerial information such as progress, productivity, and safety.

2.Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes

• Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes
• 发表于COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING/Volume32, Issue2-February 2017
• 期刊2022年影响因子/JCR分区：10.066/Q1
• This article presents a method for estimating the productivity of soil removal by combining two technologies based on computer vision: photogrammetry and video analysis.
• 本文提出了一种结合基于计算机视觉的摄影测量和视频分析两种技术估算土壤清除效率的方法。
• Photogrammetry is applied to create a time series of point clouds throughout excavation, which are used to measure the volume of the excavated soil for daily estimates of productivity. Video analysis is used to generate statistics regarding the construction activities for estimating productivity at finer time scales, when combined with the output from the photogrammetry pipeline.
• 在整个开挖过程中，摄影测量被应用于创建时间序列的点云，用于测量开挖土壤的体积，以便每日估算生产力。当结合摄影测量管道的输出时，视频分析被用来生成关于建筑活动的统计数据，以在更细的时间尺度上估计生产力。

3.Excavation equipment classification based on improved MFCC features and ELM

• Excavation equipment classification based on improved MFCC features and ELM
• 发表于NEUROCOMPUTING/Volume 261, 25 October 2017
• 期刊2022年影响因子/JCR分区：5.779/Q2
• An efficient algorithm for earthmoving device recognition is essential for underground high voltage cable protection in the mainland of China.Utilizing acoustic signals generated either by engine or the clash dur-ing operations, an intelligent classification system for four representative excavation equipments (namely, electric hammers, hydraulic hammers, cutting machines, and excavators) is developed in this paper.
• 一种高效的土方设备识别算法对于中国大陆地下高压电缆保护很关键。本文利用开挖过程中发动机或碰撞产生的声信号，开发了一种针对四种代表性开挖设备(电锤、液压锤、切割机和挖掘机)的智能分类系统。

4.Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction

• Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction
• 发表于Advanced Engineering Informatics/Volume 50, October 2021
• 期刊2022年影响因子/JCR分区：7.862/Q1
• In this paper, we combine computer vision with Long-Short Term Memory (LSTM) to predict unsafe behaviours from videos automatically.
• 在本文中，我们将计算机视觉与长短期记忆(LSTM)相结合，自动预测视频中的不安全行为。
• Our proposed approach for predicting unsafe behaviour is based on: (1) tracking people using a SiamMask; (2) predicting the trajectory of people using an improved Social-LSTM; and (3) predicting unsafe behaviour using Franklin’s point inclusion polygon (PNPoly) algorithm.
• 我们提出的预测不安全行为的方法是基于:(1)使用SiamMask追踪人群;(2)利用改进的Social-LSTM预测人群的运动轨迹;(3)利用富兰克林点包含多边形(PNPoly)算法预测不安全行为。

5.Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

• Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
• 发表于Automation in Construction/Volume 99, March 2019
• 期刊2022年影响因子/JCR分区：10.517/Q1
• As a proactive safety measure against struck-by hazards, the authors present an Unmanned Aerial Vehicle (UAV)-assisted visual monitoring method that can automatically measure proximities among construction entities.
• 作者提出了一种一种主动的安全措施——无人机辅助的可视化监测方法，可以自动测量施工实体之间的邻近性。
• To attain this end, this research conducts two research thrusts: (i) object localization using a deep neural network, YOLO-V3; and (ii) development of an image rectification method that allows for the measurement of actual distance from a 2D image collected from a UAV.
• 为此，本研究进行了两个研究方向:(i)利用深度神经网络YOLO-V3进行目标定位;和(ii)开发一种图像校正方法，允许测量从无人机收集的2D图像的实际距离。

其他.A Smart Construction Object (SCO)-Enabled Proactive Data Management System for Construction Equipment Management

• 发表于JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2017
• 期刊2022年影响因子/JCR分区：5.802/Q1
• Building on previous studies on smart construction objects (SCOs), this paper aims to develop a SCO-enabled proactive big data management system to facilitate the data collection, data visualization and data analysis for construction equipment management.
• 本文旨在在前人对智能施工对象(SCOs)研究的基础上，开发一个基于SCOs的主动大数据管理系统，以方便施工设备管理的数据收集、数据可视化和数据分析。