Inception-Det: large aspect ratio rotating object detector for remote sensing images

One important area of research in the field of remote sensing information processing is image object detection. Finding expected objects in an image and returning their category confidence and bounding boxes is the objective of the visual object detection task. Remote sensing images, in contrast to conventional images, contain many objects with large aspect ratios and are densely distributed, posing numerous challenging issues. In this paper, we propose Inception-Det, which makes use of a two-stage detection head design to solve these issues. The first stage is used to predict rotational anchors close to the GT-Box, and the second stage uses a higher positive IoU threshold and complete features calculated by FPN Inception to get better detection results. By effectively resolving the issue of objects with large aspect ratios being difficult to regress to high-precision bounding boxes, our proposed feature-complete transform (FCT) can effectively extend the detailed information contained in the feature map without introducing background noise. Extensive testing on two publicly available datasets, DOTA and HRSC2016, demonstrates that our proposed method outperforms the alternatives and further enhances detection performance at a rapid rate.