Rotate-Yolov5 for Aerial Images

H. Chen, F. X. Liu*, X. L. Huang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

In Recent years, great progress has made in object detection. However, since the orientation of object in aerial image is random, the regular horizontal object detection method is not suitable for aerial images. In this paper, we present a Rotate-Yolov5 network based on Yolov5. We use an Adaptive Rotating Anchor Generation Module (ARAGM) to generate anchors with object orientation information. Then the orientation information is used for Rotate-Deformable Convolution Module (R-DCM) to extract features. Finally, we use a decouple detection head as Oriented Object Detection Module (OODM) to yield classification and regression results. Moreover, Rotate-Smooth L1 is used to optimize the loss function. We evaluate the proposed Rotate-Yolov5 on DOTA datasets and the mAP reached 75.4, which demonstrate the superiority of its effectiveness.

Original languageEnglish
Article number012038
JournalJournal of Physics: Conference Series
Volume2278
Issue number1
DOIs
Publication statusPublished - 1 Jun 2022
Event2022 6th International Conference on Machine Vision and Information Technology, CMVIT 2022 - Virtual, Online
Duration: 25 Feb 2022 → …

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