Paper Title
object detection yolo v3
Article Identifiers
Authors
kasham malini , shivani gattu
Keywords
object detection , image classification, feature extraction , bounding boxes , convolutional neural networks
Abstract
In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 1 is added, which reduced the number of weight parameters of the layers. Extensive experiments on Pascal VOC datasets 2007/2012 showed that the proposed method achieved better performance
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How To Cite
"object detection yolo v3", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 2, page no.a318-a322, February-2023, Available :https://ijnrd.org/papers/IJNRD2302040.pdf
Issue
Volume 8 Issue 2, February-2023
Pages : a318-a322
Other Publication Details
Paper Reg. ID: IJNRD_186240
Published Paper Id: IJNRD2302040
Downloads: 000121168
Research Area: Computer Science & TechnologyÂ
Country: Hyderabad, TELANGANA STATE, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2302040.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2302040
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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