A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery
Xi Gu, Lingbin Kong, Zhicheng Wang, Jie Li, Zhaohui Yu, Gang Wei

TL;DR
This paper introduces CenterFPANet, a lightweight, anchor-free object detection framework for optical remote sensing images that balances high speed and accuracy through a novel FPA module and efficient backbone design.
Contribution
The paper presents a new lightweight, anchor-free detector with a novel FPA module and asymmetric revolution block, improving detection speed and accuracy for remote sensing images.
Findings
Achieves 64.00% mAP on DOTA dataset
Runs at 22.2 FPS, close to current accuracy levels
Outperforms Faster R-CNN in speed by 60.87%
Abstract
With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we designed the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods
