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EFN: Field-Based Object Detection for Aerial Images
Content Provider | MDPI |
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Author | Liu, Jin Zheng, Haokun |
Copyright Year | 2020 |
Description | Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images. |
Starting Page | 3630 |
e-ISSN | 20724292 |
DOI | 10.3390/rs12213630 |
Journal | Remote Sensing |
Issue Number | 21 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-11-05 |
Access Restriction | Open |
Subject Keyword | Remote Sensing Imaging Science High Resolution Remote Sensing Image Object Detection Instance Semantic Segmentation Field-based Network Oriented Bounding Box |
Content Type | Text |
Resource Type | Article |