Vehicle Detection under Adverse Weather from Roadside LiDAR Data

Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.

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Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp026 ◽

2009 ◽

Vol 66 (6) ◽

pp. 1023-1028 ◽

Cited By ~ 7

Author(s):

James H. Churnside ◽

Eirik Tenningen ◽

James J. Wilson

Keyword(s):

Data Processing ◽

Airborne Lidar ◽

Lidar Data ◽

Norwegian Sea ◽

Fish Schools ◽

Processing Algorithms ◽

High Level ◽

Processing Techniques ◽

Data Processing Algorithms ◽

Broad Scale

Abstract Churnside, J. H., Tenningen, E., and Wilson, J. J. 2009. Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea. – ICES Journal of Marine Science, 66: 1023–1028. A broad-scale lidar survey was conducted in the Norwegian Sea in summer 2002. Since then, various data-processing techniques have been developed, including manual identification of fish schools, multiscale median filtering, and curve fitting of the lidar profiles. In the automated techniques, applying a threshold to the data, as carrried out already to eliminate plankton scattering, has been demonstrated previously to improve the correlation between lidar and acoustic data. We applied these techniques to the lidar data of the 2002 survey and compared the results with those of a mackerel (Scomber scombrus) survey done by FV “Endre Dyrøy” and FV “Trønderbas” during the same period. Despite a high level of variability in both lidar and trawl data, the broad-scale distribution of fish inferred from the lidar agreed with that of mackerel caught by the FV “Endre Dyrøy”. This agreement was obtained using both manual and automated processing of the lidar data. This work is the first comparison of concurrent lidar and trawl surveys, and it demonstrates the utility of airborne lidar for mackerel studies.

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Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Sensors ◽

10.3390/s21030750 ◽

2021 ◽

Vol 21 (3) ◽

pp. 750

Author(s):

Iván Garrido ◽

Jorge Erazo-Aux ◽

Susana Lagüela ◽

Stefano Sfarra ◽

Clemente Ibarra-Castanedo ◽

...

Keyword(s):

Deep Learning ◽

Data Processing ◽

Cultural Heritage ◽

State Of The Art ◽

Automated Analysis ◽

Data Interpretation ◽

Thermal Image ◽

Destructive Testing ◽

Processing Algorithms ◽

Data Processing Algorithms

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

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