object detection yolo v3

Registration ID: IJNRD_186240

Published ID: IJNRD2302040

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kasham malini , shivani gattu

object detection , image classification, feature extraction , bounding boxes , convolutional neural networks

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

Published Paper   E-Certificate

kasham malini & shivani gattu (February-2023). object detection yolo v3. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(2), a318-a322. https://ijnrd.org/papers/IJNRD2302040.pdf

Volume 8 Issue 2, February-2023

Pages : a318-a322

Paper Reg. ID: IJNRD_186240

Published Paper Id: IJNRD2302040

Downloads: 000122253

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Hyderabad, TELANGANA STATE, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2302040.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2302040

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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