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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Gong, Yifu Lin, Zhibin Wang, Jinhui Gong, Na Department of Electrical and Computer Engineering North Dakota State University FargoND United States Department of Civil and Environmental Engineering North Dakota State University FargoND United States

Welding is commonly used for connecting metal components in these critical metallic infrastructure, such as agricultural facilities, wind turbines, railways, bridges and pipelines. However, welding processes vulnerably lead to forming cracks, pores, and other defects on the surface. These defects not only could result in severer cracks and corrosion, but also may ultimately lead to malfunction and failure of metal components. Inspection of welds is thus critical to ensure the welding quality during fabrication, construction process, and later in-service stage. The visual inspection is the crucial and most cost-effective step to determine if the welding quality is passed or rejected. However, fast and accurately determining welding quality is a challenging task in the conventional visual inspection process, which is highly dependent on the experience and expertise of inspectors, and it is fairly subjective and sometimes even misleading. To meet the gap, we bring machine intelligence to welding visual inspection. Specifically, we developed a low-cost portable embedded device to support advanced machine learning algorithms for real-time welding image processing. © 2018, Society for Imaging Science and Technology.

关键词： Learning algorithms

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Incremental Two-Network Approach to Develop a Purity Analyzer System for Canola Seeds

Incremental Two-Network Approach to Develop a Purity Analyze...

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IS and T International Symposium on Electronic Imaging: intelligent robotics and industrial applications using computer vision, iriacv 2022

作者： Singh, Kuldeep Saccon, Fernando Joseph, Dileepan Department of Electronics and Communication Malaviya National Institute of Technology Jaipur India Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

Given a suitable dataset, transfer learning using deep convolutional neural networks is an effective method to develop a system to detect and classify objects. Despite having models pretrained on large general-purpose datasets, the requirement to manually label an application-specific dataset remains a limiting factor in system development. We consider this wider problem in the context of the purity analysis of canola seeds, where end users wish to distinguish species of interest from contaminants in images taken with optical microscopes. We use a Detector network, trained only to detect seeds, to help label the dataset used to train an Analyzer network, capable of both seed detection and classification. We present results, over three experiments that involve 25 contaminant species, including Primary and Secondary Noxious Weed Seeds (as per the Canadian Weed Seeds Order), to validate our incremental approach. We also compare the proposed system to competing ones in a literature review. © 2022, Society for Imaging Science and Technology.

关键词： Large dataset

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Discriminating the presence of the cerebral aneurysm using shape features obtained from medical images of the cerebral vessel

Discriminating the presence of the cerebral aneurysm using s...

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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Kikuchi, Kosei Yagi, Takanobu Rong, Xu Ohya, Jun Department of Modern Mechanical Engineering WASEDA University Japan WASEDA University. Japan

Towards the establishment of the preventive medical care for the cerebral aneurysm, this paper proposes an SVM based method for building a discrimination function that classifies the presence or absence of the cerebral aneurysm using the cerebral blood vessel's shape features obtained from medical images such as MR images. using the discrimination function, this paper explores how much each feature affects the onset of the cerebral aneurysm. This paper deals with the internal carotid artery (ICA). The blood vessel (ICA)'s shape features are extracted from medical images of 18 persons without cerebral aneurysm and 13 patients with a cerebral aneurysm. From the medical image, the cross sections and centerline of the ICA are obtained. The cross sections are divided into nine sections along the centerline. Shape features such as the cross sectional area, its circularity, curvature, torsion, length of the centerline and branch angles are obtained in each section;as a total, 113 features including the mean and variance of some features in each section are used for building the SVM. As a result of conducting the experiments, the accuracy for discriminating the presence/absence of the aneurysm by the SVM is 90.3%. In the obtained discrimination function, the coefficient values of the function can be considered how much the features affect the onset of the aneurysm. The features that could significantly cause the onset of the cerebral aneurysm are clarified, and the reasons why these features are significant are discussed. © 2018, Society for Imaging Science and Technology.

关键词： Blood vessels

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Selection of construction waste using sensor fusion

Selection of construction waste using sensor fusion

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2019 intelligent robotics and industrial applications using computer vision Conference, iriacv 2019

作者： Nyumura, Masaya Bao, Yue Tokyo City University Tokyo Japan

In recent years, buildings in urban areas are frequently being demolished for a variety of reasons. For example, demolitions happen when buildings are rebuilt because the building was sold during an asset sale by a corporation in financial difficulties, because the building was built during the period of high economic growth and was aging, or because the building was damaged in a natural disaster, which happens frequently in recent years. However, construction waste is still being sorted by hand. Therefore, it is desired to reduce labor costs and to improve safety of workers. In order to overcome these issues, this study investigated methods for automatically recognizing waste materials. However, these methods had several problems, such as low recognition accuracy and an inability to handle metals, such as iron. In this research, we propose a method for automatically recognizing waste materials using sensor fusion. In the proposed method, information regarding the color, brightness, and shape of the object is acquired from images obtained using imaging sensors. In addition, we also focus on differences in the thermal conductivity of different materials and use a thermal sensor to measure the temperature of the target object to obtain thermal information. We performed a material recognition experiment in which only camera images were used, and a material recognition experiment in which sensor fusion was used. The results show that the recognition accuracy was approximately 10% higher overall in the experiment conducted using the latter method compared to the experiment conducted using the former method. These results show that the proposed method is effective. © 2019, Society for Imaging Science and Technology.

关键词： Disasters

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Predicting rapid fire growth (flashover) using conditional generative adversarial networks

Predicting rapid fire growth (flashover) using conditional g...

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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Yun, Kyongsik Bustos, Jessi Lu, Thomas Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: Firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred. © 2018, Society for Imaging Science and Technology.

关键词： computer vision

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Rare-class extraction using cascaded pretrained networks applied to crane classification

Rare-class extraction using cascaded pretrained networks app...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Klomp, Sander R. Brouwers, Guido M.Y.E. Wijnhoven, Rob G.J. De With, Peter H.N. Eindhoven University of Technology SPS-VCA group of Electr. Eng. Eindhoven Netherlands ViNotion b.v. Eindhoven Netherlands

Overweight vehicles are a common source of pavement and bridge damage. Especially mobile crane vehicles are often beyond legal per-axle weight limits, carrying their lifting blocks and ballast on the vehicle instead of on a separate trailer. To prevent road deterioration, the detection of overweight cranes is desirable for law enforcement. As the source of crane weight is visible, we propose a camera-based detection system based on convolutional neural networks. We iteratively label our dataset to vastly reduce labeling and extensively investigate the impact of image resolution, network depth and dataset size to choose optimal parameters during iterative labeling. We show that iterative labeling with intelligently chosen image resolutions and network depths can vastly improve (up to 70×) the speed at which data can be labeled, to train classification systems for practical surveillance applications. The experiments provide an estimate of the optimal amount of data required to train an effective classification system, which is valuable for classification problems in general. The proposed system achieves an AUC score of 0.985 for distinguishing cranes from other vehicles and an AUC of 0.92 and 0.77 on lifting block and ballast classification, respectively. The proposed classification system enables effective road monitoring for semi-automatic law enforcement and is attractive for rare-class extraction in general surveillance classification problems. © 2020 Society for Imaging Science and Technology.

关键词： Deterioration

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Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

Estimating vehicle fuel economy from overhead camera imagery...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Karnowski, Thomas Tokola, Ryan Oesch, Sean Eicholtz, Matthew Price, Jeff Gee, Tim Electrical and Electronics Systems Research Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science Florida Southern College LakelandFL33801 United States GRIDSMART Technologies Inc. KnoxvilleTN37931 United States

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Data set (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the data set utility through three applications: estimation of fuel consumption based on segmentation bounding boxes, vehicle discrimination for vehicles with large bounding boxes, and fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set of web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results demonstrate the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras. © 2020 Society for Imaging Science and Technology.

关键词： computer vision

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Connected-tube MPP model for unsupervised 3D fiber detection

Connected-tube MPP model for unsupervised 3D fiber detection

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Li, Tianyu Aguilar, Camilo G. Agyei, Ronald F. Hanhan, Imad A. Sangid, Michael D. Comer, Mary L. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Aeronautics and Astronautics Purdue University West LafayetteIN United States

In this paper, we extend our previous 2D connected-tube marked point process (MPP) model to a 3D connected-tube MPP model for fiber detection. In the 3D case, a tube is represented by a cylinder model with two spherical areas at its ends. The spherical area is used to define connection priors that encourage connection of tubes that belong to the same fiber. Since each long fiber can be fitted by a series of connected short tubes, the proposed model is capable of detecting curved long tubes. We present experimental results on fiber-reinforced composite material images to show the performance of our method. © 2020, Society for Imaging Science and Technology.

关键词： Fibers

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Virtual Simulation of the Artificial Satellites Based on OpenGL 10th

Virtual Simulation of the Artificial Satellites Based on Ope...

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10th International Conference on intelligent robotics and applications (ICIRA

作者： Liu, Yang Gu, Yikun Xie, Zongwu Yang, Haitao Wang, Zhichao Liu, Hong Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9783319652986;9783319652979

In order to generate the images of the satellites in outer space, a virtual simulation platform was build up based on Visual C++ and OpenGL API. Take the Sinosat-2 Satellite as an example, which is a failure satellite of China. Its projection model was established using computer-graphics technology. Its kinematics formulas were described using quaternions to avoid singularity. A spinning model was set up to demonstrate its motion in 3D space. The platform could simulate the synthetic images of the satellite and then send them to the virtual serve system through TCP/IP in real time.

关键词： Binocular vision Virtual reality OpenGL SINOSAT-2 DFH-4 Spinning model

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Robotic assembly of smartphone back shells with eye-in-hand visual servoing

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robotics AND computer-INTEGRATED MANUFACTURING 2018年 50卷 102-113页

作者： Chang, Wen-Chung Natl Taipei Univ Technol Dept Elect Engn Taipei Tech Box 2125 Taipei 10608 Taiwan

Research concerning smartphone assembly has attracted increasing attention from the manufacturing industry. This paper presents an automatic back shell assembly system using real-time visual servoing in an eye-in-hand configuration. Look-then-move and look-and-move control approaches are sequentially proposed to handle distal and proximal assembly tasks, respectively. During distal assembly, binocular vision is employed to assist a robotic manipulator to locate and reconstruct the positions and orientations of a cellular phone and its back shell in the work space. After lifting the back shell up and then reaching the top of the cellular phone, there is no binocular field of view. A seemingly novel way of 3-D reconstruction by two uniocular fields of view of the two cameras is proposed. With the pair of monocular vision sensors, two monocular visual servo control laws are employed sequentially to perform the proximal assembly task autonomously. Based on the experimental results, the robotic manipulator can be driven to hold the back shell by a vacuum absorption device and position it precisely onto the smartphone to accomplish the automatic assembly task even if the vision system is only roughly calibrated. The proposed control approach for distal and proximal assembly appears to be efficient, flexible, and reliable for potential applications in industrial manufacturing. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Eye-in-hand vision Manipulation Robotic assembly Real-time vision Visual servoing

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