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

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 20368

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Special Issue Editors

Prof. Dr. Manuel Graña

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Guest Editor

Computational Intelligence Group, Universidad del Pais Vasco, San Sebastian, Spain
Interests: hyperspectral image analysis; computational intelligence; medical imaging
Special Issues, Collections and Topics in MDPI journals

Dr. Jose Manuel Lopez-Guede

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Guest Editor

Automatic Control and Systems Engineering, University of the Basque Country, Vitoria, Spain
Interests: drones; mobility; artificial intelligence; energy; traffic; optimization; modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Anna Kamińska-Chuchmała

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Guest Editor

Wrocław University of Science and Technology, Poland
Interests: geostatistical methods; LiDAR data processing; remote sensing

Dr. Paweł Ksieniewicz

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Guest Editor

Wrocław University of Science and Technology, Poland
Interests: hyperspectral image analysis; computational intelligence; LiDAR data processing; soft computing
Special Issues, Collections and Topics 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

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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.

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Published Papers (5 papers)

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Research

21 pages, 3920 KiB

Open AccessArticle

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

by Marcos Alonso, Daniel Maestro, Alberto Izaguirre, Imanol Andonegui and Manuel Graña

Sensors 2021, 21(21), 7024; https://doi.org/10.3390/s21217024 - 23 Oct 2021

Cited by 4 | Viewed by 2752

Abstract

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature. Full article

(This article belongs to the Special Issue Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement)

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14 pages, 2998 KiB

Open AccessCommunication

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

by Emmanouil A. Varouchakis, Anna Kamińska-Chuchmała, Grzegorz Kowalik, Katerina Spanoudaki and Manuel Graña

Sensors 2021, 21(9), 3132; https://doi.org/10.3390/s21093132 - 30 Apr 2021

Cited by 8 | Viewed by 3348

Abstract

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area. Full article

(This article belongs to the Special Issue Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement)

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23 pages, 1458 KiB

Open AccessArticle

A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

by Hugo Álvarez, Marcos Alonso, Jairo R. Sánchez and Alberto Izaguirre

Sensors 2021, 21(3), 765; https://doi.org/10.3390/s21030765 - 24 Jan 2021

Cited by 8 | Viewed by 4509

Abstract

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm. Full article

(This article belongs to the Special Issue Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement)

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20 pages, 45309 KiB

Open AccessArticle

Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation

by Marcos Alonso, Alberto Izaguirre, Imanol Andonegui and Manuel Graña

Sensors 2020, 20(18), 5441; https://doi.org/10.3390/s20185441 - 22 Sep 2020

Cited by 4 | Viewed by 3692

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 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

(This article belongs to the Special Issue Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement)

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21 pages, 3769 KiB

Open AccessArticle

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

by Lipei Chen, Cheng Xu, Shuai Lin, Siqi Li and Xiaohan Tu

Sensors 2020, 20(8), 2224; https://doi.org/10.3390/s20082224 - 15 Apr 2020

Cited by 16 | Viewed by 4922

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 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

(This article belongs to the Special Issue Soft Computing, Machine Learning and Computational Intelligence for Laser Based Sensing and Measurement)

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