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

Abstract

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.

arXiv commentAccepted to IEEE Transactions on Geoscience and Remote Sensing

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• ${\mathbf{C}}^2$Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
• Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
• PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
• Oriented object detection in optical remote sensing images using deep learning: a survey
• Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
• Learning to Reduce Information Bottleneck for Object Detection in Aerial Images
• Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery
• MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images
• Towards Toxic and Narcotic Medication Detection with Rotated Object Detector