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.

翻译：在遥感领域，我们常采用朝向包围框（OBB）来限定目标。该方法能显著减少密集检测框间的重叠，并最小化包围框中背景内容的包含。为提升朝向目标的检测精度，受模板匹配任务中相似性度量函数的启发，我们提出了一种基于边缘梯度的独特损失函数。在此过程中，我们解决了函数不可微问题，以及真实标注框（GT）与预测框（PB）梯度向量间的语义对齐问题。实验结果表明，与基线算法中常用的Smooth L1损失相比，我们提出的损失函数实现了0.6%的平均精度（mAP）提升。此外，我们设计了一种基于边缘的自注意力模块，以引导检测网络更加关注目标边缘。借助这两项创新，我们在DOTA数据集上获得了1.3%的mAP提升。