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GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model
The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137530/ https://www.ncbi.nlm.nih.gov/pubmed/37190375 http://dx.doi.org/10.3390/e25040587 |
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author | Dai, Yuan Liu, Weiming |
author_facet | Dai, Yuan Liu, Weiming |
author_sort | Dai, Yuan |
collection | PubMed |
description | The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and substantial computational requirements. These limitations have led to low detection accuracy, poor generalization, and slow inference speeds. To overcome these challenges, the present study proposed a new lightweight detection model named Global and Local You-Only-Look-Once Lite (GL-YOLO-Lite), which integrates both global and local contextual information by incorporating transformer and attention modules into the popular object-detection framework YOLOv5. Specifically, a stem module replaced the original inefficient focus module, and rep modules with re-parameterization technology were introduced. Furthermore, a lightweight detection head was developed to reduce the number of redundant channels in the model. Finally, we constructed a large-scale, well-formatted FPD dataset (FPDD). The proposed model employed a binary cross-entropy (BCE) function to calculate the classification and confidence losses. An experimental evaluation of the FPDD and Pascal VOC dataset demonstrated that GL-YOLO-Lite outperformed other state-of-the-art models with significant margins, achieving 2.4–18.9 mean average precision (mAP) on FPDD and 1.8–23.3 on the Pascal VOC dataset. Moreover, GL-YOLO-Lite maintained a real-time processing speed of 56.82 frames per second (FPS) on a Titan Xp and 16.45 FPS on a HiSilicon Kirin 980, demonstrating its effectiveness in real-world scenarios. |
format | Online Article Text |
id | pubmed-10137530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101375302023-04-28 GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model Dai, Yuan Liu, Weiming Entropy (Basel) Article The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and substantial computational requirements. These limitations have led to low detection accuracy, poor generalization, and slow inference speeds. To overcome these challenges, the present study proposed a new lightweight detection model named Global and Local You-Only-Look-Once Lite (GL-YOLO-Lite), which integrates both global and local contextual information by incorporating transformer and attention modules into the popular object-detection framework YOLOv5. Specifically, a stem module replaced the original inefficient focus module, and rep modules with re-parameterization technology were introduced. Furthermore, a lightweight detection head was developed to reduce the number of redundant channels in the model. Finally, we constructed a large-scale, well-formatted FPD dataset (FPDD). The proposed model employed a binary cross-entropy (BCE) function to calculate the classification and confidence losses. An experimental evaluation of the FPDD and Pascal VOC dataset demonstrated that GL-YOLO-Lite outperformed other state-of-the-art models with significant margins, achieving 2.4–18.9 mean average precision (mAP) on FPDD and 1.8–23.3 on the Pascal VOC dataset. Moreover, GL-YOLO-Lite maintained a real-time processing speed of 56.82 frames per second (FPS) on a Titan Xp and 16.45 FPS on a HiSilicon Kirin 980, demonstrating its effectiveness in real-world scenarios. MDPI 2023-03-29 /pmc/articles/PMC10137530/ /pubmed/37190375 http://dx.doi.org/10.3390/e25040587 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dai, Yuan Liu, Weiming GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title | GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title_full | GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title_fullStr | GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title_full_unstemmed | GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title_short | GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model |
title_sort | gl-yolo-lite: a novel lightweight fallen person detection model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137530/ https://www.ncbi.nlm.nih.gov/pubmed/37190375 http://dx.doi.org/10.3390/e25040587 |
work_keys_str_mv | AT daiyuan glyololiteanovellightweightfallenpersondetectionmodel AT liuweiming glyololiteanovellightweightfallenpersondetectionmodel |