FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. Th...

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Autores principales:	Ming-Hwa Sheu, S. M. Salahuddin Morsalin, Jia-Xiang Zheng, Shih-Chang Hsia, Cheng-Jian Lin, Chuan-Yu Chang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fuzzy guided scale choice fuzzy sigmoid function vehicle detection fuzzy logic vehicle class counting and intelligent AIoT vehicles application Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ecbaca4a0aa54a0cabbbf4ddcbf7b907
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id	oai:doaj.org-article:ecbaca4a0aa54a0cabbbf4ddcbf7b907
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spelling	oai:doaj.org-article:ecbaca4a0aa54a0cabbbf4ddcbf7b9072021-11-11T19:19:22ZFGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting10.3390/s212173991424-8220https://doaj.org/article/ecbaca4a0aa54a0cabbbf4ddcbf7b9072021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7399https://doaj.org/toc/1424-8220The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The ‘FGSC’ blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The ‘FGSC’ blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.Ming-Hwa SheuS. M. Salahuddin MorsalinJia-Xiang ZhengShih-Chang HsiaCheng-Jian LinChuan-Yu ChangMDPI AGarticlefuzzy guided scale choicefuzzy sigmoid functionvehicle detectionfuzzy logicvehicle class countingand intelligent AIoT vehicles applicationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7399, p 7399 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy guided scale choice fuzzy sigmoid function vehicle detection fuzzy logic vehicle class counting and intelligent AIoT vehicles application Chemical technology TP1-1185
spellingShingle	fuzzy guided scale choice fuzzy sigmoid function vehicle detection fuzzy logic vehicle class counting and intelligent AIoT vehicles application Chemical technology TP1-1185 Ming-Hwa Sheu S. M. Salahuddin Morsalin Jia-Xiang Zheng Shih-Chang Hsia Cheng-Jian Lin Chuan-Yu Chang FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
description	The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The ‘FGSC’ blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The ‘FGSC’ blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.
format	article
author	Ming-Hwa Sheu S. M. Salahuddin Morsalin Jia-Xiang Zheng Shih-Chang Hsia Cheng-Jian Lin Chuan-Yu Chang
author_facet	Ming-Hwa Sheu S. M. Salahuddin Morsalin Jia-Xiang Zheng Shih-Chang Hsia Cheng-Jian Lin Chuan-Yu Chang
author_sort	Ming-Hwa Sheu
title	FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
title_short	FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
title_full	FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
title_fullStr	FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
title_full_unstemmed	FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
title_sort	fgsc: fuzzy guided scale choice ssd model for edge ai design on real-time vehicle detection and class counting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ecbaca4a0aa54a0cabbbf4ddcbf7b907
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FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

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