Oriented Object Detection by Searching Corner Points in Remote Sensing Imagery

Oriented object detection in remote sensing images has drawn great attention since it can provide more accurate bounding boxes. We propose a one-stage anchor-free network based on searching four corner points of an object, which can yield an arbitrary quadrilateral to fit objects with different shapes and orientations. We detect the corners by combining two strategies, where one regresses to the relative corner positions with respect to their corresponding center and the other directly detects the absolute corner positions from the corner heatmaps. By defining a candidate corner region based on the regressed results, we check whether corner points from the corner heatmaps are included in the region. If so, the closest one relative to the regressed corner is selected as the final position; otherwise, the regressed corner position is utilized. Experiments were conducted on two aerial remote sensing datasets, and the results demonstrated that the proposed method achieves superior performance to both the anchor-based and anchor-free methods.