@article{MTMT:34742506,
	title = {An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach with Grid Structure and Shallow Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34742506},
	author = {Dowajy, Mohammad and Somogyi, József Árpád and Barsi, Árpád and Lovas, Tamás},
	doi = {10.1109/ACCESS.2024.3372431},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {12},
	unique-id = {34742506},
	issn = {2169-3536},
	abstract = {Automatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehensive approach for using shallow neural networks to segment non-urban road point clouds to support autonomous driving applications. The proposed approach involves converting raw point cloud data into a regular grid of cells or partial clouds. Initially, a shallow neural network based on cells’ properties (cell plane fitting error, cell average intensity, cell elevation range, and cell weighted density) was employed to extract road cells from raw point cloud data. The road cells were refined and segmented into inside-road and road border point clouds based on morphologic operations. A point-wise shallow neural network was used to extract road points from the border point clouds based on intensity and geometric features (roughness, curvature, and change rate of the normal). A precise road surface point cloud is obtained by merging the inside-road and filtered border point clouds. The method performance was evaluated for two datasets captured using a mobile laser scanner (MLS). In the first dataset, the road points were extracted at average completeness, correctness, quality, and overall accuracy of 98.40%, 99.13%, 97.56%, and 98.47%, respectively. Similarly, the method achieved high scores for the second dataset with 97.22% completeness, 99.02% correctness, 96.29% quality, and 98.71% overall accuracy. The method performance demonstrates an advancement when compared to various state-of-the-art methods and also confirms its adaptability to different road environments. Authors},
	keywords = {CELLS; NEURAL NETWORKS; NEURAL NETWORKS; Feature extraction; Feature extraction; Cytology; data mining; data mining; NEURAL-NETWORKS; Surface topography; Surface topography; Roads and streets; ROAD; Geometric features; Roads; Three dimensional displays; Three-dimensional displays; features extraction; 3D point cloud; 3D point cloud; Geometric feature; Weighted density; Weighted density; road segmentation; road segmentation; Point-clouds; Three-dimensional display; Point cloud compression; Point cloud compression; shallow neural network; shallow neural network; cell point cloud; plan fitting; Cell point cloud; Plan fitting},
	year = {2024},
	eissn = {2169-3536},
	pages = {33035-33044},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470; Barsi, Árpád/0000-0002-0298-7502; Lovas, Tamás/0000-0001-6588-6437}
}

@inproceedings{MTMT:34033754,
	title = {Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements},
	url = {https://m2.mtmt.hu/api/publication/34033754},
	author = {Somogyi, József Árpád and Tettamanti, Tamás and Varga, Pál and Szalay, Zsolt and Baranyai, Dániel and Lovas, Tamás},
	booktitle = {NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium},
	doi = {10.1109/NOMS56928.2023.10154403},
	unique-id = {34033754},
	year = {2023},
	pages = {1-6},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470; Tettamanti, Tamás/0000-0002-8934-3653; Szalay, Zsolt/0000-0002-6172-5772; Baranyai, Dániel/0000-0001-9674-1548; Lovas, Tamás/0000-0001-6588-6437}
}

@article{MTMT:33701095,
	title = {Comparative analysis of Road Scanning Techniques},
	url = {https://m2.mtmt.hu/api/publication/33701095},
	author = {Dowajy, Mohammad and Baranyai, Dániel and Somogyi, József Árpád and Vrbovszki, Robert and Lovas, Tamás},
	doi = {10.55779/ng31111},
	journal-iso = {NOVA GEODESIA},
	journal = {NOVA GEODESIA},
	volume = {3},
	unique-id = {33701095},
	abstract = {A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.},
	year = {2023},
	eissn = {2810-2754},
	pages = {111},
	orcid-numbers = {Baranyai, Dániel/0000-0001-9674-1548; Somogyi, József Árpád/0000-0002-7247-4470; Lovas, Tamás/0000-0001-6588-6437}
}

@article{MTMT:33535817,
	title = {Testing the measurability of steel sections with terrestrial laser scanners},
	url = {https://m2.mtmt.hu/api/publication/33535817},
	author = {Somogyi, József Árpád and SZABO-LEONE, Akos and Lovas, Tamás},
	doi = {10.55779/ng2466},
	journal-iso = {NOVA GEODESIA},
	journal = {NOVA GEODESIA},
	volume = {2},
	unique-id = {33535817},
	abstract = {When assessing the health of steel structures, capturing, and modelling the geometry is especially important. Point cloud-based technologies have special requirements; previous studies revealed certain challenges that are to be resolved. In this paper, we aimed to develop a method to investigate the effects that the surface reflectance, incidence angle, and distance have on the quality of the point cloud of steel sections. A controlled environment was established for the research, where three terrestrial laser scanners were used to measure four different steel specimens. For validation, we also made reference measurements with a structured light scanner. Due to a large amount of data, a workflow with own routines has been developed for processing the prepared measurement datasets. For standard steel sections, the comparative study clearly showed a significant influence of the section shape, resulting in occlusion and unfavorable incidence angles. Of the devices tested, the one de-signed for high-precision measurements showed the intensity highlighting phenomenon for highly reflective surfaces, however, the measurements demonstrate that with careful selection of measurement conditions and a few pre-processing steps, the technology is well suited for the assessment of steel structures.},
	year = {2022},
	eissn = {2810-2754},
	pages = {66},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470; Lovas, Tamás/0000-0001-6588-6437}
}

@article{MTMT:33105263,
	title = {Steels Specimens’ Inspection with Structured Light Scanner},
	url = {https://m2.mtmt.hu/api/publication/33105263},
	author = {Somogyi, József Árpád and Lovas, Tamás and Szabó-Leone, Ákos and Fehér, András},
	doi = {10.3311/PPci.20081},
	journal-iso = {PERIOD POLYTECH CIV ENG},
	journal = {PERIODICA POLYTECHNICA-CIVIL ENGINEERING},
	volume = {66},
	unique-id = {33105263},
	issn = {0553-6626},
	abstract = {With the recent rapid advances in technology, the use of 3D scanning systems in the engineering world has become more and more prevalent thanks to the ease of use, the improved data collection process, and the increase in the accuracy of the acquired data. During the rebuilding of the Tisza bridge on the M4 motorway, the contractor discovered that the plates used to build the steel superstructure had developed corrosion damage during several years of storage. Plates with a tolerable level of corrosion were intended to be used, but the question was how the increased surface roughness will affect the fatigue life of the plates and the welded steel fabrications made from the plates. As part of this test, fatigue specimens were measured from the material to be used for the bridge and welded with two different geometries with the help of a structured light 3D scanner (SLS scanner). This paper discusses the measurement and inspection of these steel specimens of a highway bridge, before and after the fatigue test of the parts. From the acquired data we examined defects on the surface of the parts, physical deformations by comparing measured data to a CAD model and calculated the amount of material which was lost during stress testing.},
	keywords = {3D model; Structured light scanner; geometry inspection},
	year = {2022},
	eissn = {1587-3773},
	pages = {1241-1247},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470; Lovas, Tamás/0000-0001-6588-6437}
}

@article{MTMT:32863488,
	title = {Monostori híd építésének életútkövetése},
	url = {https://m2.mtmt.hu/api/publication/32863488},
	author = {Somogyi, József Árpád and Tar, László and Vári, Barnabás and Fehér, András},
	journal-iso = {MAGÉSZ ACÉLSZERKEZETEK},
	journal = {MAGÉSZ ACÉLSZERKEZETEK},
	volume = {19},
	unique-id = {32863488},
	issn = {1785-4822},
	year = {2022},
	pages = {28},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470}
}

@article{MTMT:32863476,
	title = {Kövesgyűri gyártócsarnok lézerszkenneres mérése},
	url = {https://m2.mtmt.hu/api/publication/32863476},
	author = {Somogyi, József Árpád and Tar, László and Fehér, András},
	journal-iso = {MAGÉSZ ACÉLSZERKEZETEK},
	journal = {MAGÉSZ ACÉLSZERKEZETEK},
	volume = {19},
	unique-id = {32863476},
	issn = {1785-4822},
	year = {2022},
	pages = {73},
	orcid-numbers = {Somogyi, József Árpád/0000-0002-7247-4470}
}

@article{MTMT:32862943,
	title = {A Monostori híd mérőrendszere gyártástól a monitoring rendszerig},
	url = {https://m2.mtmt.hu/api/publication/32862943},
	author = {Völgyi, István Krisztián and Joó, Attila László and Hegyi, Péter and Kollár, Dénes and Somogyi, József Árpád},
	journal-iso = {MAGÉSZ ACÉLSZERKEZETEK},
	journal = {MAGÉSZ ACÉLSZERKEZETEK},
	volume = {19},
	unique-id = {32862943},
	issn = {1785-4822},
	year = {2022},
	pages = {20-27},
	orcid-numbers = {Völgyi, István Krisztián/0000-0001-7561-0522; Joó, Attila László/0000-0002-8010-7021; Hegyi, Péter/0000-0002-7230-5968; Kollár, Dénes/0000-0002-0048-3327; Somogyi, József Árpád/0000-0002-7247-4470}
}

@misc{MTMT:32820034,
	title = {Point Cloud Based Road Surface Modelling and Assessment},
	url = {https://m2.mtmt.hu/api/publication/32820034},
	author = {Lovas, Tamás and Baranyai, Dániel and Somogyi, József Árpád},
	doi = {10.3311/BMEZalaZONE2022-015},
	unique-id = {32820034},
	year = {2022},
	orcid-numbers = {Lovas, Tamás/0000-0001-6588-6437; Baranyai, Dániel/0000-0001-9674-1548; Somogyi, József Árpád/0000-0002-7247-4470}
}

@article{MTMT:32791236,
	title = {OpenCRG models from different data sources to support vehicle simulations},
	url = {https://m2.mtmt.hu/api/publication/32791236},
	author = {Lovas, Tamás and Ormándi, Tamás and Somogyi, József Árpád and Baranyai, Dániel and Tihanyi, Viktor Roland and Tettamanti, Tamás},
	doi = {10.1109/ACCESS.2022.3168287},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {10},
	unique-id = {32791236},
	issn = {2169-3536},
	abstract = {Digital twins of road surfaces support multiple engineering applications. Remote sensing technologies provide information from the entire surface of the pavement by high accuracy point clouds. Pavement errors and differences from designed geometry can be detected and assessed using such datasets, while OpenCRG models derived from point clouds support transportation applications. High-resolution CRG (Curved Regular Grid) models enable analyzing vehicle suspension systems in vehicle dynamics simulation environments. Furthermore, such models also support creating the digital twins of vehicle suspensions and improve the development and research of models related to vehicle dynamics. The paper presents how the suspension digital twin was obtained applying a genetic algorithm and how it was assessed. The quality of raw data and that of the derived methods are analyzed in the case of multiple mapping technologies (terrestrial, mobile, and aerial laser scanning). CRG models were created from all datasets, and their applicability was investigated to support vehicle simulations with high accuracy demand. Other important vehicle-related use cases are also mentioned in the paper.},
	year = {2022},
	eissn = {2169-3536},
	pages = {42690-42698},
	orcid-numbers = {Lovas, Tamás/0000-0001-6588-6437; Ormándi, Tamás/0000-0002-7897-8573; Somogyi, József Árpád/0000-0002-7247-4470; Baranyai, Dániel/0000-0001-9674-1548; Tettamanti, Tamás/0000-0002-8934-3653}
}