  Object detection in optical remote sensing images is an important and
challenging task. In recent years, the methods based on convolutional neural
networks have made good progress. However, due to the large variation in object
scale, aspect ratio, and arbitrary orientation, the detection performance is
difficult to be further improved. In this paper, we discuss the role of
discriminative features in object detection, and then propose a Critical
Feature Capturing Network (CFC-Net) to improve detection accuracy from three
aspects: building powerful feature representation, refining preset anchors, and
optimizing label assignment. Specifically, we first decouple the classification
and regression features, and then construct robust critical features adapted to
the respective tasks through the Polarization Attention Module (PAM). With the
extracted discriminative regression features, the Rotation Anchor Refinement
Module (R-ARM) performs localization refinement on preset horizontal anchors to
obtain superior rotation anchors. Next, the Dynamic Anchor Learning (DAL)
strategy is given to adaptively select high-quality anchors based on their
ability to capture critical features. The proposed framework creates more
powerful semantic representations for objects in remote sensing images and
achieves high-performance real-time object detection. Experimental results on
three remote sensing datasets including HRSC2016, DOTA, and UCAS-AOD show that
our method achieves superior detection performance compared with many
state-of-the-art approaches. Code and models are available at
https://github.com/ming71/CFC-Net.
