NDLI: Tiny YOLO Optimization Oriented Bus Passenger Object Detection

Content Provider	IET Digital Library
Author	Zhang, Shuo Wu, Yanxia Men, Chaoguang Li, Xiaosong
Abstract	The real-time collection of bus passenger object detection is an essential part of developing a smart bus system. The difficulty of object detection mainly lies in the objective factors, such as: clothing, hair style and accessories, light, etc. Traditional object detection methods with the artificial feature extraction suffers from insufficient strength in expression, generalization, and recognition rate. The object detection method based on deep learning mainly uses the convolutional neural network in deep learning to learn features from a large set of data. The learned features can describe the rich information inherent in the data, and improve the expression ability of the features as well as the recognition accuracy. Due to too many parameters of the Convolutional neural network (CNN) model, the amount of calculation is too large to be operated on the vehicle terminal. To reduce calculation burden and improve the operation speed, we employs the depthwise separable convolution method to optimize the convolutional layer of tiny YOLO network model. It decomposes a complete convolution operation into depthwise convolution and pointwise convolution, thus reducing the parameter amount of the CNN and improving the operation speed. The experiment results reveal that the speed of bus passenger object detection detected by our improved model is 4 times faster than the previous one but with the nearly same detection accuracy.
Starting Page	132
Ending Page	138
Page Count	7
ISSN	10224653
Volume Number	29
e-ISSN	20755597
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/29/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2019.11.002
Journal	Chinese Journal of Electronics
Publisher Date	2020-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Artificial Feature Extraction Suffers Complete Convolution Operation Computer Vision And Image Processing Technique Convolution Convolutional Neural Network Model Deep Learning Depthwise Convolution Depthwise Separable Convolution Method Detection Accuracy Feature Extraction Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Object Detection Object Detection Method Objective Factors Operation Speed Optical, Image And Video Signal Processing Pointwise Convolution Smart Bus System Tiny YOLO Network Model Traditional Object Detection Method
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Multi-scale deep neural network for salient object detection

Application of convolution neural network object detection algorithm in logistics warehouse

Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Adaptive learning feature pyramid for object detection

Chest X-ray image denoising method based on deep convolution neural network

Tiny YOLO Optimization Oriented Bus Passenger Object Detection

Similar Documents

DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Multi-scale deep neural network for salient object detection

Application of convolution neural network object detection algorithm in logistics warehouse

Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Adaptive learning feature pyramid for object detection

Chest X-ray image denoising method based on deep convolution neural network

Tiny YOLO Optimization Oriented Bus Passenger Object Detection