Hybrid Network Model: TransConvNet for Oriented Object Detection in Remote Sensing Images

The complexity of backgrounds, the diversity of object scale and orientation, and the defects of convolutional neural network (CNN) have always been the challenges of oriented object detection in remote sensing images (RSIs). This paper designs a hybrid network model to meet these challenges and further improve the effect of oriented object detection. The inductive bias of CNN makes the network translation invariant, but it is difficult to adapt to RSIs with arbitrary object direction. Therefore, this paper designs a hybrid network, TransConvNet, which integrates the advantages of CNN and self-attention-based network, pays more attention to the aggregation of global and local information, makes up for the lack of rotation invariability of CNN with strong contextual attention, and adapts to the arbitrariness of the object direction of RSIs. In addition, to resolve the influence of complex backgrounds and multi-scale, an adaptive feature fusion network (AFFN) is designed to improve the information representation ability of feature maps with different resolutions. Finally, the adaptive weight loss function is used to train the network to further improve the effect of object detection. Extensive experimental results on the DOTA, UCASAOD, and VEDAI data sets demonstrate the effectiveness of the proposed method.

Automated summary

This paper proposes a hybrid network model, TransConvNet, to address the challenges in oriented object detection in remote sensing images. It integrates the advantages of CNN and self-attention-based network, pays attention to global and local information aggregation, and adapts to the object direction variability. The proposed method demonstrates effectiveness through extensive experimental results.