A real-time object detection algorithm for video. (July 2019)

• Record Type: Journal Article
• Authors: Lu, Shengyu; Wang, Beizhan; Wang, Hongji; Chen, Lihao; Linjian, Ma; Zhang, Xiaoyan
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 398
• Page End: 408
• Publication Date: 2019-07
• Subjects: Object detection -- GoogleNet -- YOLO -- Real-time -- Video; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.05.009
• Languages: English
• ISSNs: 0045-7906

Abstract

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.