DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seong, Jaehwan | ko |
dc.contributor.author | Kim, Hyung-soo | ko |
dc.contributor.author | Jung, Hyung-Jo | ko |
dc.date.accessioned | 2023-11-27T06:01:33Z | - |
dc.date.available | 2023-11-27T06:01:33Z | - |
dc.date.created | 2023-11-27 | - |
dc.date.issued | 2023-10 | - |
dc.identifier.citation | SENSORS, v.23, no.20 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/10203/315243 | - |
dc.description.abstract | According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as 'a danger area of a collision', and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, 'a danger area of a collision' is proposed for evaluating equipment collision risk based on the moving equipment's speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the 'danger area of a collision', thereby mitigating accident risks on construction sites. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | The Detection System for a Danger State of a Collision between Construction Equipment and Workers Using Fixed CCTV on Construction Sites | - |
dc.type | Article | - |
dc.identifier.wosid | 001095276500001 | - |
dc.identifier.scopusid | 2-s2.0-85175272952 | - |
dc.type.rims | ART | - |
dc.citation.volume | 23 | - |
dc.citation.issue | 20 | - |
dc.citation.publicationname | SENSORS | - |
dc.identifier.doi | 10.3390/s23208371 | - |
dc.contributor.localauthor | Jung, Hyung-Jo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | construction management | - |
dc.subject.keywordAuthor | safety in construction site | - |
dc.subject.keywordAuthor | collision warning | - |
dc.subject.keywordAuthor | CCTV | - |
dc.subject.keywordAuthor | deep-learning-based object detection | - |
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