DiagonalNet: Confidence diagonal lines for the UAV detection. Issue 9 (18th June 2019)

• Record Type: Journal Article
• Authors: Hu, Qintao; Duan, Qianwen; Mao, Yao; Zhou, Xi; Zhou, Guozhong
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 14:Issue 9(2019)
• Page Start: 1364
• Page End: 1371
• Publication Date: 2019-06-18
• Subjects: object detection -- deep learning -- UAV -- hourglass -- SENet; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22938
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : Due to the maneuverability and small size of the Unmanned aerial vehicles (UAVs), the traditional object detection method cannot meet the requirements of detection accuracy and real‐time performance. To address this dilemma, we propose an object detection method by using the improved hourglass network as its backbone network generating the confidence diagonal lines as detection result, and then getting the bounding box through this diagonal line. As a one‐stage method, it has a good real‐time performance. At the same time, due to the good performance of our improved network, its mean average precision performs well. Through experiments with the UAV dataset, compared with faster‐regions‐with‐convolutional‐neural‐network and You only look once (YOLO), we show that the proposed framework can quickly and accurately detect objects. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.