Deep learning and ubiquitous systems for disabled people detection using YOLO models
DC Field | Value | Language |
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dc.contributor.author | Alruwaili, Madallah | - |
dc.contributor.author | Siddiqi, Muhammad Hameed | - |
dc.contributor.author | Atta, Muhammad Nouman | - |
dc.contributor.author | Arif, Mohammad | - |
dc.date.accessioned | 2024-03-19T12:30:22Z | - |
dc.date.available | 2024-03-19T12:30:22Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 0747-5632 | - |
dc.identifier.issn | 1873-7692 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90740 | - |
dc.description.abstract | Differently -disabled people having the disorders like paralysis, limb deficiency Amelia, or amputee. Various work was done on detecting and tracking the differently-abled people for the perception of people and their mobility aids. Different combination of Fast R-CNN and Faster R-CNN with Red Green and Blue or Depth (RGB or RGB-D) camera were used. There are several state-of-the-art deep learning models e. g. Yolo and its different variants that detect and track perception of people. This research uses detection and tracking the differently-abled people with paralysis, limb deficiency Amelia, or amputee. The research purposed Yolov5 (You Only Look Once, Version 5) model of deep learning with a camera to detect and track the differently-abled people in real-time having the disorders. A Ubiquitous system for the detection of differently-abled people using deep Learning based YOLO (You Only Look Once) models could be a powerful and inclusive application of technology. This research is concerned with the RGB images dataset that has seventeen thousand and seventy-nine PNG files with fifteen thousand two hundred and seventy-eight YML annotation files. This data has five classes such as pedestrian, person in wheelchair, person pushing a person in a wheelchair, person using crutches, and person using a walking frame. From overall simulation result it shows that Yolov3 perform better than Yolov5 which detect differently-abled people with P of 0.915, R of value 0.919, and mAP@.5 of 0.951. While Yolov5 has detect differently-abled people with P of 0.885, R of value 0.887, and mAP@.5 of 0.942. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Deep learning and ubiquitous systems for disabled people detection using YOLO models | - |
dc.type | Article | - |
dc.identifier.wosid | 001170557400001 | - |
dc.identifier.doi | 10.1016/j.chb.2024.108150 | - |
dc.identifier.bibliographicCitation | COMPUTERS IN HUMAN BEHAVIOR, v.154 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85183453285 | - |
dc.citation.title | COMPUTERS IN HUMAN BEHAVIOR | - |
dc.citation.volume | 154 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Disabled people | - |
dc.subject.keywordAuthor | FastR-CNN | - |
dc.subject.keywordAuthor | FasterR-CNN | - |
dc.subject.keywordAuthor | RGB images | - |
dc.subject.keywordAuthor | YOLOv5 | - |
dc.subject.keywordAuthor | YOLOv3 | - |
dc.subject.keywordPlus | HUMAN-BEHAVIOR | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalWebOfScienceCategory | Psychology, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Psychology, Experimental | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
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