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- Tanvir Ahmad
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
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- Yinglong Ma
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
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- Muhammad Yahya
- Department of Electrical and Electronics, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia
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- Belal Ahmad
- Embedded and Pervasive Computing (EPIC) Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
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- Shah Nazir
- Department of Computer Science, University of Swabi, Ambar, Khyber Pakhtunkhwa, Pakistan
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- Amin ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu, China
説明
<jats:p>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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>∗</mml:mi></mml:math> 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.</jats:p>
収録刊行物
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- Scientific Programming
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Scientific Programming 2020 1-10, 2020-06-06
Hindawi Limited
