A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery
This work addresses the problem of efficient object detection in remote sensing imagery for applications like surveillance or mapping, but it is incremental as it builds on existing anchor-free methods with specific improvements.
The paper tackles the challenge of detecting objects quickly and accurately in complex optical remote sensing images by proposing CenterFPANet, an anchor-free object detector that achieves 64.00% mAP and 22.2 FPS on the DOTA dataset, balancing speed and accuracy.
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 detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection.