## 1 Introduction  
Object detection in optical remote sensing images (ORSIs) is a crucial but challenging task for remote sensing technology and has been widely applied in many fields, such as military, natural resources exploration, urban construction, agriculture and mapping [1, 2]. The development of a cost-effective detector considering the characteristic of ORSIs is the persistently pursued direction, and has attracted a large amount of attention from scholars and practitioners. The approaches for object detection can be roughly divided into traditional detectors and deep learning (DL)-based detectors. DL-based detectors, especially convolutional neural network (CNN) detectors, have gradually replaced traditional detectors since they possess better adaptability and generalization in different application scenarios. There are two categories of DL-based detectors: one-stage [3, 4, 5, 6, 7, 8, 9] and two-stage [10, 11, 12, 13]. The one-stage detectors directly regress bounding boxes and probabilities for each object simultaneously without region proposals; thus, they perform well regarding inference speed. Two-stage detectors employ the region proposals to improve the location and detection accuracy, with the sacrifice of the inference speed. With the emergence of large-scale natural scene images (NSIs) datasets for object detection tasks such as Pascal VOC [14] and MS COCO [15],DL-based detectors have been further developed for a better tradeoff between accuracy and cost, including Faster-RCNN [12], single shot multibox detector (SSD) [3], the series of You Only Look Once (YOLO) [4; 5; 6; 8], CenterNet [7], EfficientDet [9] and RetinaNet [16]. These detectors with continuous improvement have been widely applied in various natural scene visual detection tasks. Since ORSIs are photographed from an overhead perspective at different heights, whereas NSIs are shot from a horizontal perspective at relatively close distance, three main differences have emerged as follows: first, the available feature of most detected objects in ORSIs is less obvious than that in NSIs and leads to greater inter-class similarity. Second, the intra-class difference is more prominent since object scales of the same category in ORSIs usually vary greater. Third, the background in ORSIs is more complex and abundant than that in NSIs. Differences between ORSIs and NSIs with instances are shown in Figure 1. These differences make object detection in ORSIs more difficult, and most of the well-designed detectors for NSIs are not elaborately optimized for ORSIs. For the problems of a greater intra-class difference and inter-class similarity caused by the characteristic of objects in ORSIs, the detector needs to extract more abundant object features with high-level semantics to overcome it. However, the feature of objects in ORSIs are easily submerged by the redundant and complex background information and thus will decrease or even disappear when transmitted in the detector. Thus, DL-based detectors also require a stronger feature extraction and transmission ability. With the popularity and wide application of embedded devices such as unmanned aerial vehicles (UAVs), the demand for real-time optical remote sensing object detection deployed on edge devices has increased rapidly. UAVs with far less computing resource and storage space than computers involve wide application scenarios such as rescue, military and surveying tasks, which require a high detection accuracy, flexible equipment deployment and less inference time for detectors [17]. In recent years, several outstanding achievements have been made by researchers in fields related to ORSIs, and can be roughly divided into heavyweight [18; 19; 20; 21] and lightweight detectors [22; 23; 24; 25].