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.