Visualization and Object Detection Based on Event Information
A dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a single event can only...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1839 |
| _version_ | 1797618258723995648 |
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| author | Yinghong Fang Yongjie Piao Xiaoguang Xie Miao Li Xiaodong Li Haolin Ji Wei Xu Tan Gao |
| author_facet | Yinghong Fang Yongjie Piao Xiaoguang Xie Miao Li Xiaodong Li Haolin Ji Wei Xu Tan Gao |
| author_sort | Yinghong Fang |
| collection | DOAJ |
| description | A dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a single event can only indicate that the increase or decrease in light exceeds the threshold at a certain pixel position and a certain moment. In order to further study the ability and characteristics of event information to represent targets, this paper proposes an event information visualization method with adaptive temporal resolution. Compared with methods with constant time intervals and a constant number of events, it can better convert event information into pseudo-frame images. Additionally, in order to explore whether the pseudo-frame image can efficiently complete the task of target detection according to its characteristics, this paper designs a target detection network named YOLOE. Compared with other algorithms, it has a more balanced detection effect. By constructing a dataset and conducting experimental verification, the detection accuracy of the image obtained by the event information visualization method with adaptive temporal resolution was 5.11% and 4.74% higher than that obtained using methods with a constant time interval and number of events, respectively. The average detection accuracy of pseudo-frame images in the YOLOE network designed in this paper is 85.11%, and the number of detection frames per second is 109. Therefore, the effectiveness of the proposed visualization method and the good performance of the designed detection network are verified. |
| first_indexed | 2024-03-11T08:11:37Z |
| format | Article |
| id | doaj.art-25b90c05afe345789a7e03af55e05773 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:11:37Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-25b90c05afe345789a7e03af55e057732023-11-16T23:06:55ZengMDPI AGSensors1424-82202023-02-01234183910.3390/s23041839Visualization and Object Detection Based on Event InformationYinghong Fang0Yongjie Piao1Xiaoguang Xie2Miao Li3Xiaodong Li4Haolin Ji5Wei Xu6Tan Gao7Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaA dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a single event can only indicate that the increase or decrease in light exceeds the threshold at a certain pixel position and a certain moment. In order to further study the ability and characteristics of event information to represent targets, this paper proposes an event information visualization method with adaptive temporal resolution. Compared with methods with constant time intervals and a constant number of events, it can better convert event information into pseudo-frame images. Additionally, in order to explore whether the pseudo-frame image can efficiently complete the task of target detection according to its characteristics, this paper designs a target detection network named YOLOE. Compared with other algorithms, it has a more balanced detection effect. By constructing a dataset and conducting experimental verification, the detection accuracy of the image obtained by the event information visualization method with adaptive temporal resolution was 5.11% and 4.74% higher than that obtained using methods with a constant time interval and number of events, respectively. The average detection accuracy of pseudo-frame images in the YOLOE network designed in this paper is 85.11%, and the number of detection frames per second is 109. Therefore, the effectiveness of the proposed visualization method and the good performance of the designed detection network are verified.https://www.mdpi.com/1424-8220/23/4/1839event informationdynamic vision sensorvisualizationobject detection |
| spellingShingle | Yinghong Fang Yongjie Piao Xiaoguang Xie Miao Li Xiaodong Li Haolin Ji Wei Xu Tan Gao Visualization and Object Detection Based on Event Information Sensors event information dynamic vision sensor visualization object detection |
| title | Visualization and Object Detection Based on Event Information |
| title_full | Visualization and Object Detection Based on Event Information |
| title_fullStr | Visualization and Object Detection Based on Event Information |
| title_full_unstemmed | Visualization and Object Detection Based on Event Information |
| title_short | Visualization and Object Detection Based on Event Information |
| title_sort | visualization and object detection based on event information |
| topic | event information dynamic vision sensor visualization object detection |
| url | https://www.mdpi.com/1424-8220/23/4/1839 |
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