飞机掩体是关键的飞机防护工事，利用遥感影像实现飞机掩体的快速准确检测有重要意义。为探究遥感影像飞机掩体检测方法，收集了60个包含飞机掩体的机场信息及Google Earth影像，构建了一个飞机掩体高分辨率遥感影像数据集。对比Faster R-CNN、SSD、RetinaNet、YOLOv3和YOLOX等5个深度学习目标检测模型的综合性能，结果表明，在飞机掩体影像数据集上YOLOX模型表现更佳，平均精度可达97.7%，但水平框的检测结果无法获得飞机掩体的精确边界和朝向。为此，对YOLOX模型进行改进，提出针对不同朝向下的飞机掩体检测新方法R-YOLOX，实现对飞机掩体的旋转检测，旋转预测框更加贴合目标轮廓，采用KL 散度损失改进后的模型精度显著提升，准确率提升了7.24个百分点，对飞机掩体具有更好的检测效果。从水平框和旋转框这2个角度都能实现飞机掩体的准确检测，为高分辨率遥感影像中飞机掩体的准确识别提供了新思路。

Aircraft bunkers are the key aircraft protection fortifications. Therefore, the use of remote sensing images to achieve rapid and accurate detection of aircraft bunkers is of great significance. To develop a method for detecting aircraft bunkers through remote sensing images, we collected information and Google Earth images of 60 airfields with aircraft bunkers and constructed a high-resolution remote-sensing-image dataset of aircraft bunkers. Then, we compared the comprehensive performance of five deep-learning target-detection models, namely, Faster R-CNN, SSD, RetinaNet, YOLOv3, and YOLOX. The research results show that the YOLOX model performs better on the aircraft-bunkers-image dataset with an average precision of 97.7%. However, the results of the horizontal frame cannot obtain a precise boundary and orientation of the aircraft bunkers. Therefore, we propose a new method R-YOLOX, which is an improved version of the YOLOX model, for detecting aircraft bunkers under different orientations. Our method achieves the rotational detection of aircraft bunkers. Compared with the YOLOX model, the rotational prediction frame of our method fits the target contour more closely, and the model accuracy with respect to Kullback-Leibler divergence loss is significantly improved, with an increase of 7.24 percentage points, showing a better detection effect on aircraft bunkers. Further, the accurate detection of aircraft bunkers is achieved from the perspective of horizontal and rotating frames, thereby providing a new idea for the accurate identification of aircraft bunkers in remote sensing images.