Feature Enhanced Centernet for Object Detection in Remote Sensing Images

Multi-scale object detection in optical remote sensing imagery is a challenging task due to the varied object scales. Existed state-of-art object detection methods have achieved significant growth. However, most of the methods are based on default anchors, which need to be predefined. The multi-scale object detection accuracy still needs to be improved, especially for small and dense objects. To improve the robustness of the detection algorithm and the performance of multi-scale object detection, a novel anchor-free multi-scale object detection method Feature Enhanced CenterNet is proposed in this paper. First, we use the "encoder-decoder" structure and introduce horizontal connections to enhance feature representation capabilities. Second, an context-aware up-sampling method is proposed to obtain feature maps with suitable scale. To demonstrate the performance of the proposed method, we perform abundant experiments on the public remote sensing datasets. The experimental results demonstrate the robustness and effectiveness of the proposed method.