English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Rotation object detection has achieved significance progress in the community of remote sensing image processing. In contrast to horizontal bounding boxes, rotating bounding boxes require an accurate angle parameter to position the object. However, angle regression accuracy remains an urgent challenge due to the arbitrary orientation, large aspect ratio, and dense alignment of aerial images. To solve this problem, we propose an anchor-free network with angle attention (FAA-Net), which delicately develops two core designs,  i.e , angle feature extraction (AFE) module and angle prediction head (APH) module. Specifically, the AFE module performs feature extraction and angle selection to reduce the negative effects of angle adaptation. In addition, APH module is further employed to better predict the object orientation by introducing the frequency attention and integral operation. Extensive experiments on two classical benchmark datasets have demonstrated that our method can effectively boost detection performance against several start-of-the-art approaches.

Learning an anchor-free network with angle attention for remote sensing object detection