Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets.

翻译：卫星遥感图像的自动目标检测对于资源勘探和自然灾害评估具有重要意义。为解决遥感图像检测中存在的现有问题，本文提出了一种改进的YOLOX模型，用于卫星遥感图像的自动检测。该模型被命名为RS-YOLOX。为增强网络的特征学习能力，我们在YOLOX的主干网络中使用了高效通道注意力机制，并将自适应空间特征融合与YOLOX的颈部网络相结合。为平衡训练中正负样本的数量，我们使用了Varifocal Loss损失函数。最后，为获得高性能的遥感目标检测器，我们将训练好的模型与一个名为切片辅助超推理的开源框架相结合。本工作在三个航空遥感数据集上评估了模型。我们的对比实验表明，我们的模型在遥感图像数据集的目标检测中具有最高的准确率。