CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images

Understand

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.