  In the field of remote sensing, we often utilize oriented bounding boxes
(OBB) to bound the objects. This approach significantly reduces the overlap
among dense detection boxes and minimizes the inclusion of background content
within the bounding boxes. To enhance the detection accuracy of oriented
objects, we propose a unique loss function based on edge gradients, inspired by
the similarity measurement function used in template matching task. During this
process, we address the issues of non-differentiability of the function and the
semantic alignment between gradient vectors in ground truth (GT) boxes and
predicted boxes (PB). Experimental results show that our proposed loss function
achieves $0.6\%$ mAP improvement compared to the commonly used Smooth L1 loss
in the baseline algorithm. Additionally, we design an edge-based self-attention
module to encourage the detection network to focus more on the object edges.
Leveraging these two innovations, we achieve a mAP increase of 1.3% on the DOTA
dataset.
