In addition, affected by illumination, shadow, scale variations, resolution, and so on, objects in RSIs are difficult to detect accurately using deep learning methods directly. 4. Many geospatial objects in RSIs are combinations of some objects, such as airports, schools, and thermal power plants. These composite objects contain several parts and the feature representation is not fixed. It is challenging to detect composite objects due to their diverse appearance, irregular boundaries, and complex background. Based on the problems mentioned above, object detection methods in RSIs have been extensively explored in the academic community. Some studies [11, 12, 13, 14] applied feature pyramid structures for multi-scale object detection. Top-down or bottom-up pathway feature layers can be used to achieve multi-scale prediction, particularly for small object detection. The attention mechanism [15] can emphasize the relative information of objects and improve the feature representation. In order to improve the capacity of discriminative features, some research [16, 17, 18] introduced an attention mechanism to CNN for object detection in RSIs and effectively improved the discrimination of features. However, attention methods are usually applied on all convolutional layers which generates a large computation requirement. Additionally, these methods are mainly built on anchor-based [8, 9] networks that need to set anchor parameters for new objects. In the field of composite object detection, some studies used hard example mining [19, 20] or improved RPN [21] for airport detection. [PERSON] et al. [22] proposed a part-based network for composite object detection and achieved great performance. However, the objects studied in these articles are different from composite objects such as primary and secondary schools (PSSs). Figure 1 shows some school samples in Gaofen (GF) images. PSSs in China are composed of a ground track field and some buildings of various shapes and sizes, which have relatively clear boundaries. In urban regions, PSSs are surrounded by clustered residential areas; but in remote rural regions, the color and texture of playgrounds are different from that in the urban areas, and PSSs are surrounded by villages and farmlands. In addition, the PSSs are relatively smaller and the internal components of PSSs are more compact compared with other composite objects. As a typical composite object, PSSs vary in terms of appearance and scale, and are easily disturbed by complex environments, which brings huge challenges for detection. Furthermore, PSSs detection plays a crucial role in social development. PSSs are the foundation of national education and shoulder the responsibility of educating the future successors of the country. Analyzing the spatial distribution of PSSs is of great significance for regional construction and economic evaluation. Therefore, PSSs detection is an important and meaningful task for applications of remote sensing images. Anchor-free networks are simple and effective methods for object detection. In general, most existing object detectors adopt anchors for generating predicted boxes, which results in an excessive number of hyper-parameters. These hyper-parameters need to be carefully tuned before training. When applying these methods to new datasets, it is necessary to analyze the sizes and aspect ratios of bounding boxes. Moreover, anchor-based detectors will generate large anchor boxes during training for achieving a high recall rate, which leads to an excessive number of negative samples. Anchor boxes also involve complicated computation by repeatedly calculating the intersection-over-union (_IoU_) between predicted boxes and ground-truth boxes [23]. Therefore, some research has investigated anchor-free networks [23, 24, 25, 26, 27]. Anchor-free detectors can be divided into two categories: methods based on key point, such as CenterNet [25], and methods based on dense box such as FCOS [23].