Visualization and Object Detection Based on Event Information

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 1839
• Main Authors: Fang, Yinghong, Piao, Yongjie, Xie, Xiaoguang, Li, Miao, Li, Xiaodong, Ji, Haolin, Xu, Wei, Gao, Tan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07-02-2023; MDPI
• Subjects: Comparative analysis; dynamic vision sensor; event information; Frames per second; Information management; Light; object detection; Optical measuring instruments; Photoreceptors; Remote sensing; Scientific visualization; Sensors; Temporal resolution; Visualization; China; dynamic vision sensor; visualization; object detection; event information
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23041839

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.