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Computer Vision for Remote Sensing and Infrastructure Inspection
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Computer Vision for Remote Sensing and Infrastructure Inspection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15066

Special Issue Editors

Dr. Alireza Tavakkoli

E-Mail Website
Guest Editor
University of Nevada, Reno
Dr. Khiem Tran

E-Mail Website
Guest Editor
University of Florida
Interests: Geophysical Testing and Nondestructive Evaluation; Foundation Design and Capacity Assessment; Numerical modeling of mechanical waves; Full waveform tomography, refraction tomography, and surface wave methods
Dr. Hung La

E-Mail Website
Guest Editor
University of Nevada, Reno
Interests: Robotics; Automation in Construction; Remote Sensing

Special Issue Information

Dear Colleagues,

With the emergence of affordable yet advanced sensors, it is becoming more and more practical to develop innovative perception capabilities for non-destructive and remote inspection and sensing appliactions. Therefore the design, implementation, and validation of novel techniques that enable autonomous measuring of important properties of remote physical enviornments and infrastructures is becoming an essential component of remote sensing terrestrial applications (e.g., forestry, carbon emission, and environmental studies) and civil infrastructure inspection protocols. The goal of this Special Issue is to provide a venue for discussions of the state-of-the-art developments in visual perception and their applications in remote sensing and infrastructure inspection, and focusing on the exploration of new frontiers in the area of visual computing for robotics with an emphasis on remote inspection and terrestrial sensing applications. Therefore, the Special Issue welcomes papers that provide reviews and surveys of the current state-of-the-art findings in the field of visual perception for remote sensing and infrastructure inpsection as well as work exploring novel approaches of using visual sensors and computing to solve problems in tele-robotics, remote sensing, and infrastructure inspection. Priority will be given to papers that integrate computer vision, virtual/augmented/mixed reality, and/or machine learning to address challenging problems facing environmental, civil, safety, and societal needs. 

Topics of interest for this Special Issue include, but are not limited, to the following areas:

  • Vision for infrastructure inspection
  • Vision for remote sensing
  • Nondestructive evaluation systems
  • Nondestructive testing systems
  • 3D environmental modeling
  • Calibration and remote sensing
  • Visual localization and navigation systems
  • Visual inspection systems and applications
  • Sensor fusion for visual perception
  • Vision for robotic systems 

The sensors to be considered, but not limited to:

  • Electrical and optical sensors
  • Infrared sensors
  • Visual sensors
  • Radar
  • Lidar
  • Multispectral sensors
  • General sensors (temperature, flow, moisture, humidity, pressure)
  • Ultrasonic sensors 

Dr. Alireza Tavakkoli
Dr. Khiem Tran
Dr. Hung La
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Remote Sensing
  • Civil Infrastructure Inspection
  • Computer Vision
  • Machine Learning
  • Image and Video Processing
  • Point-Cloud Processing

Published Papers (3 papers)

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Research

15 pages, 1381 KiB  
Article
NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images
by Mingwei Zhang, Weipeng Jing, Jingbo Lin, Nengzhen Fang, Wei Wei, Marcin Woźniak and Robertas Damaševičius
Sensors 2020, 20(18), 5292; https://doi.org/10.3390/s20185292 - 16 Sep 2020
Cited by 31 | Viewed by 3888
Abstract
The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional [...] Read more.
The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller. Full article
(This article belongs to the Special Issue Computer Vision for Remote Sensing and Infrastructure Inspection)
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13 pages, 3413 KiB  
Article
Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques
by Yarens J. Cruz, Marcelino Rivas, Ramón Quiza, Gerardo Beruvides and Rodolfo E. Haber
Sensors 2020, 20(16), 4505; https://doi.org/10.3390/s20164505 - 12 Aug 2020
Cited by 29 | Viewed by 5591
Abstract
One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This [...] Read more.
One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness. Full article
(This article belongs to the Special Issue Computer Vision for Remote Sensing and Infrastructure Inspection)
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26 pages, 94598 KiB  
Article
Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection
by Umme Hafsa Billah, Hung Manh La and Alireza Tavakkoli
Sensors 2020, 20(16), 4403; https://doi.org/10.3390/s20164403 - 7 Aug 2020
Cited by 19 | Viewed by 4971
Abstract
An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network [...] Read more.
An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures. Full article
(This article belongs to the Special Issue Computer Vision for Remote Sensing and Infrastructure Inspection)
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