# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 1 Introduction  
In recent years, object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. Object detection is essential in the field of aerial and satellite image analysis but it is also difficult. The problem is that the objects in the images are of various dimensions and sizes. In addition, these high-resolution images from planes or satellites have complex and scattered backgrounds of excessively detailed ground objects. Object detection methods using deep learning techniques have received increasing attention in recent years and as a result of this, they have achieved state-of-the-art performance [1]. Among these object detection methods, the faster region-based convolutional neural network (faster R-CNN) [2] is quite successful. This method consists of two steps. In the first step, a region proposal network (RPN) generates several hundred or thousands of candidate region proposals. In the second step, the object/non-object classification is done by feature extraction of region proposals. In the faster R-CNN method, feature extraction is performed by using a convolutional neural network (CNN) [3]. As the CNN has a fixed input sampling frame, it fails to detect objects with high complexity and clutter in remote sensing images. At this point, the deformable convolutional concept [4] is introduced. It makes convolution operation on different areas of each input sample depending on the offsets, regardless of the fixed geometric shape of the standard convolution process. It is very important to use high-resolution features to detect small objects in remote sensing images. However, these features are in the shallow CNN layers. Feature pyramid network (FPN) [5]has been introduced to extract these features. Batch normalization (BN) [6] is often used in the training phase of remote sensing images. BN achieves successful results in training with large batch sizes. However, large batches require the same amount of multiple GPU power (such as systems with 8 or 16 GPUs). Weight standardization (WS) [7] has been introduced for successful training with small batch numbers in single GPU systems. The publicly available 10-class geospatial object detection (NWPU-VHR 10) [8] dataset was used for testing the model we proposed. The studies using this data set are summarized below:  
* [PERSON] at al. [9] developed a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on the collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) [10] classifier used for the detection of objects. * [PERSON] et al. [11] proposed a novel and effective approach to learning a rotation-invariant CNN (RICNN) model for advancing the performance of object detection, which is achieved by introducing and learning a new rotation-invariant layer on the basis of the existing CNN architectures. * [PERSON] et al. [12] proposed a novel deep-learning-based object detection framework including region proposal network and local-contextual feature fusion network designed for remote sensing images. They called the proposed model the rotation insensitive and context enhanced object detection (RI-CAO) network. They developed a double-channel feature fusion network that can learn local and contextual properties along two independent pathways. * [PERSON] et al. [13] proposed an anchor-free and sliding-window-free deconvolutional region proposal network (DODN) and constructed a two-stage deconvolutional object detection network. Instead of using an anchor mechanism, they used a deconvolutional neural network followed by a connected region generation module to generate reference boxes. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low GPU systems.