Extracting dense rotating objects accurately from remote sensing images is an emerging task in object detection. To increase the applicability of existing algorithms in the above tasks, an optimized anchor-free network optimized by a dual attention mechanism (DAM) and gate multiscale feature fusion (GMFF) is designed. The DAM module is composed of two attention mechanisms with different functions. This part can enhance the backbone network’s ability to extract and model information at different levels and reduce the accuracy loss caused by object density changes in the image. The GMFF module uses the gating structure to realize adaptive transmission and integration of multiscale information. Through this module, the useless information in features will be filtered, and the key information will be retained. Several experiments are designed to verify the feasibility of the algorithm. Compared with the baseline model, adding DAM and GMFF to the dense rotating object extraction task in remote sensing images improves the model accuracy by 3.5% and 2.1%, respectively, while adding two modules simultaneously, and the accuracy increases from 79.1% to 84.3%. In conventional object extraction tasks, such as dataset for object detection in aerial images and HRSC2016, our method has the highest accuracy compared to other similar algorithms, with 76.5% and 90.3%, respectively.

中文翻译：

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用于遥感图像中密集旋转目标检测的优化无锚网络

从遥感图像中准确提取密集旋转物体是物体检测中的一项新兴任务。为了提高现有算法在上述任务中的适用性，设计了一种通过双重注意机制（DAM）和门多尺度特征融合（GMFF）优化的无锚网络。DAM模块由两种功能不同的注意力机制组成。这部分可以增强主干网络对不同层次信息的提取和建模能力，减少图像中物体密度变化带来的精度损失。GMFF模块利用门控结构实现多尺度信息的自适应传输和集成。通过该模块，过滤掉特征中的无用信息，保留关键信息。设计了多个实验来验证算法的可行性。与基线模型相比，在遥感图像中密集旋转目标提取任务中添加DAM和GMFF，模型精度分别提高了3.5%和2.1%，同时添加两个模块时，精度从79.1%提升到84.3% 。在传统的目标提取任务中，例如航空图像中的目标检测数据集和HRSC2016，我们的方法与其他类似算法相比具有最高的准确率，分别为76.5％和90.3％。