  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.
