A real-time object detection algorithm for video
Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, w...
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| Vydané v: | Computers & electrical engineering Ročník 77; s. 398 - 408 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier Ltd
01.07.2019
Elsevier BV |
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| ISSN: | 0045-7906, 1879-0755 |
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| Abstract | Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video. |
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| AbstractList | Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video. |
| Author | Chen, Lihao Linjian, Ma Wang, Hongji Wang, Beizhan Lu, Shengyu Zhang, Xiaoyan |
| Author_xml | – sequence: 1 givenname: Shengyu surname: Lu fullname: Lu, Shengyu organization: Software School, Xiamen University, Siming South Road, Xiamen City, Fujian Province, 361005, China – sequence: 2 givenname: Beizhan surname: Wang fullname: Wang, Beizhan email: wangbz@xmu.edu.cn, 3137349575@qq.com organization: Software School, Xiamen University, Siming South Road, Xiamen City, Fujian Province, 361005, China – sequence: 3 givenname: Hongji surname: Wang fullname: Wang, Hongji organization: Software School, Xiamen University, Siming South Road, Xiamen City, Fujian Province, 361005, China – sequence: 4 givenname: Lihao surname: Chen fullname: Chen, Lihao organization: Beijing University of Posts and Telecommunication, West Tucheng Road, Beijing City, Beijing, 100876, China – sequence: 5 givenname: Ma surname: Linjian fullname: Linjian, Ma organization: Software School, Xiamen University, Siming South Road, Xiamen City, Fujian Province, 361005, China – sequence: 6 givenname: Xiaoyan surname: Zhang fullname: Zhang, Xiaoyan organization: Tan Kah Kee College, Xiamen University, Siming South Road, Xiamen City, Fujian Province, 361005, China |
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| Title | A real-time object detection algorithm for video |
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