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Special Issue "Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Dr. Manuel Graña
Website
Guest Editor
Universidad del Pais Vasco, Computational Intelligence Group, San Sebastian, Spain
Interests: hyperspectral image analysis; computational intelligence; medical imaging
Special Issues and Collections in MDPI journals
Prof.Dr. Jose Manuel Lopez-Guede

Guest Editor
Univesity of the Basque Country, Spain
Interests: LiDAR data processing; image processing; control for autonomous vehicles; computational intelligence
Special Issues and Collections in MDPI journals
Dr. Anna Kamińska-Chuchmała
Website
Guest Editor
Wrocław University of Science and Technology, Poland
Interests: geostatistical methods; LiDAR data processing; remote sensing
Dr. Paweł Ksieniewicz
Website
Guest Editor
Wrocław University of Science and Technology, Poland
Interests: hyperspectral image analysis; computational intelligence; LiDAR data processing; soft computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Laser-based sensing and measurement is increasingly used in the industry and many other productive fields, like transportation and surveillance. Computer vision technologies that employ laser in some form or another are widely used in industrial inspection and quality control processes. On the other hand, LiDAR technology has been used for remote sensing with applications in agriculture, forestry and public land management. LiDAR technology also has a prominent role in the emerging transportation systems based on autonomous vehicles in situations that range from personal transportation to industrial vehicle guidance. Finally, safe LiDAR systems may increasingly be used for surveillance and crowd monitoring in public places because of their intrinsic respect for personal data. Laser-based measurements also have a prominent role in the field of flexible manufacturing and additive manufacturing. The goal of this Special Issue is to gather researchers working on the application of innovative computational methods such as deep learning to laser-generated measurement data for various purposes.

Topics

The methods and tools applied to vision and robotics include, but are not limited to, the following:

  • Computational intelligence methods;
  • Machine learning and deep learning methods;
  • Self-adaptation and self-organisation;
  • Point cloud registration methods;
  • Multimodal information fusion;
  • Hardware implementation and algorithms acceleration (GPUs, FPGA,s, etc.).

The fields of application include, but are not limited to, the following:

  • 3D scene reconstruction;
  • 3D volume visualization;
  • Gesture and posture analysis and recognition;
  • Surveillance systems in public areas;
  • Autonomous and social robots;
  • Industry 4.0: inspection and quality control;
  • Transportation systems: autonomous navigation and road inventory;
  • Remote sensing: forestry, agriculture, land management.

Prof. Manuel Graña
Prof. Jose Manuel Lopez-Guede
Dr. Anna Kamińska-Chuchmała
Dr. Paweł Ksieniewicz
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 papers will be 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 2200 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.

Published Papers (2 papers)

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Research

Open AccessArticle
Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation
Sensors 2020, 20(18), 5441; https://doi.org/10.3390/s20185441 - 22 Sep 2020
Abstract
Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional [...] Read more.
Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction. Full article
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Open AccessArticle
A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR
Sensors 2020, 20(8), 2224; https://doi.org/10.3390/s20082224 - 15 Apr 2020
Cited by 1
Abstract
The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means [...] Read more.
The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%. Full article
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