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Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety

Bahaa Eddine MNEYMNEH, Mohamad ABBAS, Hiam KHOURY

《工程管理前沿（英文）》 2018年第5卷第2期页码 227-239 doi: 10.15302/J-FEM-2018071

摘要： Construction is considered among the most dangerous industries and is responsible for a large portion of total worker fatalities. A construction worker has a probability of 1-in-200 of dying on the job during a 45-year career, mainly due to fires, falls, and being struck by or caught between objects. Hence, employers must ensure their workers wear personal protective equipment (PPE), in particular hardhats, if they are at risk of falling, being struck by falling objects, hitting their heads on static objects, or coming in proximity to electrical hazards. However, monitoring the presence and proper use of hardhats becomes inefficient when safety officers must survey large areas and a considerable number of workers. Using images captured from indoor jobsites, this paper evaluates existing computer vision techniques, namely object detection and color-based segmentation tools, used to rapidly detect if workers are wearing hardhats. Experiments are conducted and the results highlight the potential of cascade classifiers, in particular, to accurately, precisely, and rapidly detect hardhats under different scenarios and for repetitive runs, and the potential of color-based segmentation to eliminate false detections.

关键词： construction safety personal protective equipment hardhat computer vision

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基于计算机视觉的民用基础设施的检查与监测研究进展 Review

Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki

《工程（英文）》 2019年第5卷第2期页码 199-222 doi: 10.1016/j.eng.2018.11.030

摘要：

计算机视觉技术与远程摄像机和无人机（UAVs）的采集相结合，为民用基础设施状况评估提供了前景良好的非接触式解决方案。这种系统的最终目标是自动且稳健地将图像或视频数据转换为可操作的信息。本文概述了将计算机视觉技术应用于民用基础设施状态评估的最新进展。特别介绍了计算机视觉、机器学习和结构工程领域的相关研究。评估工作分为两类：检查应用和监测应用。检查应用包括识别环境，如结构构件，表征局部和全部的可见损坏，以及检测参考图像的变化。监测应用包括应变和位移的静态测量，以及模态分析的位移动态测量。最后，文章指出了为实现基于自动化视觉的民用基础设施和监测目标而持续存在的一些关键挑战，以及为解决这些挑战而正在进行的工作。

关键词：结构检查和监测人工智能计算机视觉机器学习光流

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Information fusion in aquaculture: a state-of the art review

Shahbaz Gul HASSAN,Murtaza HASAN,Daoliang LI

《农业科学与工程前沿（英文）》 2016年第3卷第3期页码 206-221 doi: 10.15302/J-FASE-2016111

摘要： Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multi-sensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing, computer vision, sensors and wireless sensor networks. Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an “industrialized” ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, pH, dissolved oxygen, turbidity, temperature etc., necessary for the fish feeding process, are examined. Moreover, the different waste management and fish disease diagnosis techniques using different technologies, expert systems and modeling are also reviewed.

关键词： aquaculture computer vision information fusion modeling sensor

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用于计算机视觉任务的光场成像技术综述 Review Article

贾晨1,2,石凡1,2,赵萌1,2,陈胜勇1,2

《信息与电子工程前沿（英文）》 2022年第23卷第7期页码 1077-1097 doi: 10.1631/FITEE.2100180

摘要：光场成像因其解决计算机视觉问题的能力而备受关注。本文首先简要回顾了近年来计算机视觉的研究进展。对于影响计算机视觉发展的大多数因素来说，视觉信息获取的丰富性和准确性起着决定性作用。光场成像技术利用照相机或微透镜阵列记录光线位置和方向信息，获取完整三维场景信息，为计算机视觉研究做出巨大贡献。光场成像提高了深度估计以及图像分割、融合和三维重建的精度。光场成像还被创新地应用于虹膜和人脸识别、材料和虚假行人识别、极平面图像采集和形状恢复以及光场显微镜。我们进一步总结了光场成像技术在计算机视觉研究中存在的问题和发展趋势，如光场数据集的建立和评估、在高动态范围条件下的应用、光场增强和虚拟现实。光场成像在各种研究中取得巨大成功。在过去25年，超过180篇文献报道了光场成像在解决计算机视觉问题上的能力。我们梳理了这些文献，使研究人员更容易搜索有关解决方案的详细方法。

关键词：光场成像；相机阵列；微透镜阵列；极平面图像；计算机视觉

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Bridging the gap: Neuro-Symbolic Computing for advanced AI applications in construction

《工程管理前沿（英文）》页码 727-735 doi: 10.1007/s42524-023-0266-0

摘要： Deep Learning (DL) has revolutionized the field of Artificial Intelligence (AI) in various domains such as computer vision (CV) and natural language processing. However, DL models have limitations including the need for large labeled datasets, lack of interpretability and explainability, potential bias and fairness issues, and limitations in common sense reasoning and contextual understanding. On the other side, DL has shown significant potential in construction for safety and quality inspection tasks using CV models. However, current CV approaches may lack spatial context and measurement capabilities, and struggle with complex safety and quality requirements. The integration of Neuro-Symbolic Computing (NSC), an emerging field that combines DL and symbolic reasoning, has been proposed as a potential solution to address these limitations. NSC has the potential to enable more robust, interpretable, and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning. The combination of symbolism and connectionism in NSC can lead to more efficient data usage, improved generalization ability, and enhanced interpretability. Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.

关键词： advanced AI in construction safety and quality inspection Neuro-Symbolic Computing Deep Learning computer vision

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建筑领域计算机视觉的效益实现管理 Views & Comments

Peter E.D. Love,Jane Matthews,Weili Fang,Hanbin Luo

《工程（英文）》 2023年第27卷第8期页码 11-13 doi: 10.1016/j.eng.2022.09.009

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

《结构与土木工程前沿（英文）》 2022年第16卷第5期页码 564-575 doi: 10.1007/s11709-022-0829-x

摘要： The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

关键词： water conveyance tunnels siltation images remotely operated vehicles deep learning ensemble learning computer vision

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Current applications of artificial intelligence for intraoperative decision support in surgery

Allison J. Navarrete-Welton, Daniel A. Hashimoto

《医学前沿（英文）》 2020年第14卷第4期页码 369-381 doi: 10.1007/s11684-020-0784-7

摘要： Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one ( =21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

关键词： artificial intelligence decision support clinical decision support systems intraoperative deep learning computer vision machine learning surgery

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A zone-layered trimming method for ceramic core of aero-engine blade based on an advanced reconfigurable laser processing system

《机械工程前沿（英文）》 2022年第17卷第2期 doi: 10.1007/s11465-022-0675-5

摘要： Ceramic structural parts are one of the most widely utilized structural parts in the industry. However, they usually contain defects following the pressing process, such as burrs. Therefore, additional trimming is usually required, despite the deformation challenges and difficulty in positioning. This paper proposes an ultrafast laser processing system for trimming complex ceramic structural parts. Opto-electromechanical cooperative control software is developed to control the laser processing system. The trimming problem of the ceramic cores used in aero engines is studied. The regional registration method is introduced based on the iterative closest point algorithm to register the path extracted from the computer-aided design model with the deformed ceramic core. A zonal and layering processing method for three-dimensional contours on complex surfaces is proposed to generate the working data of high-speed scanning galvanometer and the computer numerical control machine tool, respectively. The results show that the laser system and the method proposed in this paper are suitable for trimming complex non-datum parts such as ceramic cores. Compared with the results of manual trimming, the method proposed in this paper has higher accuracy, efficiency, and yield. The method mentioned above has been used in practical application with satisfactory results.

关键词： ceramic parts trimming computer-aided laser manufacturing 3D vision reconfigurable laser processing system

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哲学视野中的工程

殷瑞钰

《中国工程科学》 2008年第10卷第3期页码 4-8

摘要：

通过历史唯物史观的考察，指出工程是在人类生存、发展过程中的一项基本实践活动，工程在不同历史时期一直是直接生产力。工程活动是先于科学活动出现的。研究认为，有关自然的知识和活动应分为科学、技术、工程三元，科学活动的主要特征是“探索”、“发现”，技术活动的主要特征是“发明”、“创新”，工程活动的主要特征是“集成”、“构建”。从现代知识意义上看，“科学—技术—工程—产业”之间存在相关的知识链（知识网络），工程与产业的关系更直接、更紧密。工程是人类为了维持生存、繁衍和发展，为了建设家园及美好地生活而进行的实践活动，是人类智慧的凝聚和所追求理想的一种体现。从哲学视野看，工程活动的成果往往体现为构筑一个新的存在物，即在一定边界条件下优化构建起来的集成体。工程集成包涵了诸多技术要素的集成，也包括了技术要素与经济、社会、管理等方面的基本要素在一定条件下的优化—集成。在新世纪的背景条件下工程是创新活动的重要领域，也应是哲学思考的新领域，哲学的超越和工程的超越存在着诸多“交集”和“并集”，因而，工程需要哲学，哲学要面向工程，工程界与哲学界互动，是中国工程哲学兴起的特点。

关键词：工程哲学思考

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室内导航系统视觉标记性能分析 Article

Gaetano C. LA DELFA,Salvatore MONTELEONE,Vincenzo CATANIA,Juan F. DE PAZ,Javier BAJO

《信息与电子工程前沿（英文）》 2016年第17卷第8期页码 730-740 doi: 10.1631/FITEE.1500324

摘要：智能手机大规模普及，人们对可穿戴设备和物联网兴趣倍增，以及定位服务指数级增长，使得室内定位导航成为近年来最重要的技术挑战之一。室内定位系统不仅在零售行业及定向推送广告行业有着巨大的市场，同时，它还可以部署在医院、机场、博物馆等公共建筑中，成为提升人们生活质量的基础性配置。甚至，在紧急情况下，是否部署室内定位系统，会造成生死之别。文献中已报道多种方法。近年来，得益于智能手机相机性能的大幅提升，无标记点和有标记点的计算机视觉方法得到开发。在之前的研究中，我们提出了一种利用低功耗蓝牙和嵌入地面的2D视觉标记系统进行室内定位导航的技术。在本文中，我们对3种可服务于实时应用的2D视觉标记（Vuforia，ArUco标记和AprilTag）进行了定性的性能评估。本文重点研究了附于地表瓷砖的3种视觉标记在特定情况下的表现，提出了最优视觉标记的甄选原则，为我们提出的室内定位导航技术提供技术支撑。

关键词：室内定位；视觉标记；计算机视觉

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基于定量属性的单目标视觉跟踪算法评价体系研究 Article

Wen-jing KANG, Chang LIU, Gong-liang LIU

《信息与电子工程前沿（英文）》 2020年第21卷第3期页码 405-421 doi: 10.1631/FITEE.1900245

摘要：视觉跟踪是计算机视觉领域热门研究课题之一。近年来，很多先进跟踪算法和性能评价基准相继发布，并取得巨大成功。现有评价体系大多定位于衡量整体性能，无法通过针对性的详细论证评估跟踪器的优势和缺点，且很多常用评测指标缺乏令人信服的含义解释。本文从测试数据、测试方法、测试指标3方面深入分析跟踪评价体系的细节。首先，归纳整理了12个反映图像序列不同特性的帧间视觉属性，并首次定量给出其归一化公式。基于这些属性定义，提出两种新的测试方法，即基于相关性的测试和基于权重的测试，使评价体系能更直观、更清晰地评定跟踪器各方面性能。然后，将所提测试方法应用于著名的跟踪挑战赛，即Video Object Tracking (VOT) Challenge 2017。测试结果表明，在目标尺寸快速或剧烈变化时，跟踪器大多表现不佳，即使基于深度学习的先进跟踪器也未能很好解决这一问题。此外发现，中心位置差错（center location error，CLE）性能指标虽未考虑到目标尺度，在实际测试中仍对目标尺寸变化很敏感。

关键词：视觉跟踪；性能评价；视觉属性；计算机视觉

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基于形参均匀B样条盈亏修正的图像边缘检测

赵颜利,王湛,郭成昊,刘凤玉

《中国工程科学》 2007年第9卷第7期页码 65-70

摘要：

利用形参均匀B样条平滑公式，建立了一种盈亏修正的图像边缘检测新方法。首先对图像的原型值点进行盈亏修正，进一步减少原始图像和平滑图像之间的残余误差；然后利用形参均匀B样条修匀公式对修正后的图像拟合光滑曲面；最后求拟合后的光滑曲面的一阶导数极值点或二阶导数的零交叉点作为边缘特征点。试验表明，该方法稳定可靠，精度较高，能够很好地去除伪边缘；同时该方法简洁，便于实时处理。

关键词：形状参数均匀B样条边缘检测计算机视觉盈亏修正

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Machine vision-based automatic fruit quality detection and grading

《农业科学与工程前沿（英文）》 doi: 10.15302/J-FASE-2023532

摘要：

● A machine vision-based prototype system was developed for fruit grading.

关键词： Computer and machine vision convolution neural network deep learning defective fruit detection fruit grading microcontroller

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Actor-Critic强化学习算法及其在开发基于计算机视觉的界面跟踪中的应用 Article

Oguzhan Dogru, Kirubakaran Velswamy, 黄彪

《工程（英文）》 2021年第7卷第9期页码 1248-1261 doi: 10.1016/j.eng.2021.04.027

摘要：

本文通过将对象跟踪形式化为序列决策过程，使控制理论与计算机视觉实现同步。强化学习（RL）智能体成功跟踪了两种液体之间的界面，这通常是化学、石化、冶金和石油行业中跟踪的关键变量。该方法使用少于100 张图像来创建环境，智能体无需专家知识即可从中生成自己的数据。与依赖大量参数的监督学习（SL）方法不同，这种方法需要的参数少得多，这自然降低了维护成本。除了经济性外，该智能体还对环境不确定性（如遮挡、强度变化和过度噪声）具有鲁棒性。在闭环控制情境下，基于界面位置的偏差被选作训练阶段的优化目标。该方法展示了RL方法在油砂行业中的实时对象跟踪应用。本文除了介绍界面跟踪问题外，还详细回顾了最有效的RL方法之一——actor-critic策略。

关键词：界面跟踪对象跟踪遮挡强化学习均匀流形逼近和投影