## 1 Introduction
In summary, the location of objects on the image is generally determined by scanning the entire image with a sliding window approach and a classifier, which may be selected from the abovementioned methods, which does the recognition task. The classifiers trained with these methods have a low size of parameters. Therefore, scanning the entire image with small strides allows an acceptable pace at object detection. In 2012, following the remarkable success of AlexNet's [15] at the ImageNet Large-Scale Visual Recognition Challenge [16], the CNN architectures, which are also known as deep learning methods, have begun to be used in different image processing problems. After the evolution of AlexNet, which can be accepted as a milestone for deep learning, deeper architectures, such as visual geometry group (VGG) [17], GoogleNet [18], which came up with inception modules, and residual network (ResNet) [19], were developed and the error rate in the competition decreased gradually. Along with these advancements, researchers started to use CNN structures in object classification with satellite images [20; 21; 22; 23; 24]. Although the remote sensing images have less spatial details and complex background, these methods can achieve highly accurate results near the visual interpretation performance. In the challenges for object detection from natural images, competitors have tended to use state-of-the-art deep learning architectures, such as PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) and COCO (Common Objects in Context) as a base network with a large amount of labeled data to beat the previous results. They applied different approaches, fine-tuning the base networks and performing some modifications; not only for increasing the accuracy of the classification part of the object detection task but also for improving the localization performance. For the classification stage of object detection, the success of deep architectures is promising, but as they include a large number of parameters, direct use of the sliding window method, which has a high computational cost, is being abandoned. New architectures, such as the R-CNN (regions with CNN features), SPP-NET (Spatial Pyramid Pooling), Fast R-CNN, and Faster R-CNN, have emerged to overcome the computational cost disadvantage of the sliding window approach. These architectures use CNN networks as a base network for classification and solve the problem of localization by creating object candidates from the image [25; 26; 27; 28]. With these structures, high performance and high speed could be achieved in real-time applications, such as object detection from a video stream. Additionally, object proposal approaches have become more widely used in remote sensing applications, with improvements in speed and performance [29; 30; 31; 32; 33]. Detection by producing an object proposal achieved successful results, but there is a trade-off between the detection performance and processing speed according to the number of proposals produced. The number and the accuracy of the object candidates could directly affect the precision of the trained model or reduce the detection speed [34]. In recent years, You Only Look Once (YOLO) [35] and Single Shot MultiBox Detector (SSD) [36] networks, which convert the classification and localization steps of the object detection task into a regression problem, can perform object detection tasks with a single neural network structure. These new methods have also overwhelmed the object proposal techniques in major competitions, such as PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) [37] and COCO (Common Objects in Context) [38], where objects are detected from natural images. However, few studies have implemented these techniques on remotely sensed images. This is mainly due to an imbalanced dataset, where there are a large number of labeled natural images for detection tasks but less for remote sensing images.