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Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation
In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architecture of the method described in this paper is based o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914801/ https://www.ncbi.nlm.nih.gov/pubmed/35271133 http://dx.doi.org/10.3390/s22051986 |
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author | Lee, Sunwoo Lee, Dongkyu Kee, Seok-Cheol |
author_facet | Lee, Sunwoo Lee, Dongkyu Kee, Seok-Cheol |
author_sort | Lee, Sunwoo |
collection | PubMed |
description | In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architecture of the method described in this paper is based on a harmonic densely connected network and a cross-stage partial network. The dataset used for training was calibrated to four 190° wide-angle cameras to generate around view monitor (AVM) images based on the Chungbuk National University parking lot, and an experiment was performed based on this dataset. In the experimental results, the available parking area was visualized by detecting the parking line, parking area, and available driving area in the AVM images. Furthermore, the undetected area in the semantic segmentation as a collision risk area was visualized in order to obtain the results. According to the proposed attention CSPHarDNet model, the experimental results were 81.89% mIoU and 18.36 FPS in a NVIDIA Xavier environment. The results of this experiment demonstrated that algorithms can be used in real time in a parking situation and have better performance results compared to the conventional HarDNet. |
format | Online Article Text |
id | pubmed-8914801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89148012022-03-12 Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation Lee, Sunwoo Lee, Dongkyu Kee, Seok-Cheol Sensors (Basel) Article In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architecture of the method described in this paper is based on a harmonic densely connected network and a cross-stage partial network. The dataset used for training was calibrated to four 190° wide-angle cameras to generate around view monitor (AVM) images based on the Chungbuk National University parking lot, and an experiment was performed based on this dataset. In the experimental results, the available parking area was visualized by detecting the parking line, parking area, and available driving area in the AVM images. Furthermore, the undetected area in the semantic segmentation as a collision risk area was visualized in order to obtain the results. According to the proposed attention CSPHarDNet model, the experimental results were 81.89% mIoU and 18.36 FPS in a NVIDIA Xavier environment. The results of this experiment demonstrated that algorithms can be used in real time in a parking situation and have better performance results compared to the conventional HarDNet. MDPI 2022-03-03 /pmc/articles/PMC8914801/ /pubmed/35271133 http://dx.doi.org/10.3390/s22051986 Text en © 2022 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 Lee, Sunwoo Lee, Dongkyu Kee, Seok-Cheol Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title | Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title_full | Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title_fullStr | Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title_full_unstemmed | Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title_short | Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation |
title_sort | deep-learning-based parking area and collision risk area detection using avm in autonomous parking situation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914801/ https://www.ncbi.nlm.nih.gov/pubmed/35271133 http://dx.doi.org/10.3390/s22051986 |
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