FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3109613965> ?p ?o ?g. }

• W3109613965 endingPage "1055" @default.
• W3109613965 startingPage "1055" @default.
• W3109613965 abstract "Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a “single sensor”. The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a “two out of two” logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed." @default.
• W3109613965 created "2020-12-07" @default.
• W3109613965 creator A5009333545 @default.
• W3109613965 creator A5076592104 @default.
• W3109613965 date "2020-11-29" @default.
• W3109613965 modified "2023-10-04" @default.
• W3109613965 title "Object Detection at Level Crossing Using Deep Learning" @default.
• W3109613965 cites W1986795873 @default.
• W3109613965 cites W1991126109 @default.
• W3109613965 cites W1999809952 @default.
• W3109613965 cites W2083807525 @default.
• W3109613965 cites W2102625004 @default.
• W3109613965 cites W2112755387 @default.
• W3109613965 cites W2121274305 @default.
• W3109613965 cites W2194775991 @default.
• W3109613965 cites W2278234102 @default.
• W3109613965 cites W2531409750 @default.
• W3109613965 cites W2919115771 @default.
• W3109613965 cites W2963446712 @default.
• W3109613965 cites W2993564273 @default.
• W3109613965 cites W3041865473 @default.
• W3109613965 cites W3099746624 @default.
• W3109613965 cites W4246218351 @default.
• W3109613965 cites W639708223 @default.
• W3109613965 doi "https://doi.org/10.3390/mi11121055" @default.
• W3109613965 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7760285" @default.
• W3109613965 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33260336" @default.
• W3109613965 hasPublicationYear "2020" @default.
• W3109613965 type Work @default.
• W3109613965 sameAs 3109613965 @default.
• W3109613965 citedByCount "19" @default.
• W3109613965 countsByYear W31096139652021 @default.
• W3109613965 countsByYear W31096139652022 @default.
• W3109613965 countsByYear W31096139652023 @default.
• W3109613965 crossrefType "journal-article" @default.
• W3109613965 hasAuthorship W3109613965A5009333545 @default.
• W3109613965 hasAuthorship W3109613965A5076592104 @default.
• W3109613965 hasBestOaLocation W31096139651 @default.
• W3109613965 hasConcept C100776233 @default.
• W3109613965 hasConcept C111919701 @default.
• W3109613965 hasConcept C126322002 @default.
• W3109613965 hasConcept C127413603 @default.
• W3109613965 hasConcept C1975866 @default.
• W3109613965 hasConcept C22212356 @default.
• W3109613965 hasConcept C26517878 @default.
• W3109613965 hasConcept C2780615140 @default.
• W3109613965 hasConcept C38652104 @default.
• W3109613965 hasConcept C41008148 @default.
• W3109613965 hasConcept C71924100 @default.
• W3109613965 hasConcept C78519656 @default.
• W3109613965 hasConceptScore W3109613965C100776233 @default.
• W3109613965 hasConceptScore W3109613965C111919701 @default.
• W3109613965 hasConceptScore W3109613965C126322002 @default.
• W3109613965 hasConceptScore W3109613965C127413603 @default.
• W3109613965 hasConceptScore W3109613965C1975866 @default.
• W3109613965 hasConceptScore W3109613965C22212356 @default.
• W3109613965 hasConceptScore W3109613965C26517878 @default.
• W3109613965 hasConceptScore W3109613965C2780615140 @default.
• W3109613965 hasConceptScore W3109613965C38652104 @default.
• W3109613965 hasConceptScore W3109613965C41008148 @default.
• W3109613965 hasConceptScore W3109613965C71924100 @default.
• W3109613965 hasConceptScore W3109613965C78519656 @default.
• W3109613965 hasIssue "12" @default.
• W3109613965 hasLocation W31096139651 @default.
• W3109613965 hasLocation W31096139652 @default.
• W3109613965 hasLocation W31096139653 @default.
• W3109613965 hasLocation W31096139654 @default.
• W3109613965 hasOpenAccess W3109613965 @default.
• W3109613965 hasPrimaryLocation W31096139651 @default.
• W3109613965 hasRelatedWork W2329452785 @default.
• W3109613965 hasRelatedWork W2381563323 @default.
• W3109613965 hasRelatedWork W2381683256 @default.
• W3109613965 hasRelatedWork W2899084033 @default.
• W3109613965 hasRelatedWork W2924159740 @default.
• W3109613965 hasRelatedWork W568154930 @default.
• W3109613965 hasRelatedWork W573015223 @default.
• W3109613965 hasRelatedWork W580387070 @default.
• W3109613965 hasRelatedWork W599048497 @default.
• W3109613965 hasRelatedWork W79911373 @default.
• W3109613965 hasVolume "11" @default.
• W3109613965 isParatext "false" @default.
• W3109613965 isRetracted "false" @default.
• W3109613965 magId "3109613965" @default.
• W3109613965 workType "article" @default.