# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 1 Introduction  
With the development of remote sensing technology, the research of remote sensing images has been receiving remarkable attention. Meanwhile, ship and plane detection in the optical remote sensing images [1; 2; 3] plays an important role in a wild range of applications. Several breakthroughs have been witnessed in the area of large object detection with high resolution on the PASCAL VOC dataset in the past decade by the family of region-based convolutional neural networks (R-CNN) methods [4; 5; 6; 7], especially Faster R-CNN [7]. However, they usually fail to detect very small objects, as rich representations are difficult to learn from their poor-quality appearance and structure. However, the object in the optical remote sensing images usually has the characteristic of small object size, which has posed much more challenges than normal object detection and good solutions are still rare so far. Some efforts have been devoted to addressing small object detection problems. The common way [8; 9] is to increase the feature resolution of small objects by simply magnifying the input images, which often results in heavy time consumption for training and testing. Another way [10; 11] is centered on generating multi-scale representation which enhances high-level features by combining multiple lower-level features, which is to naively increase the feature dimension. This practice is not able to guarantee that the constructed features are interpretable and discriminative enough for small objects. FCN [12] combines coarse-to-fine predictions from multiple layers by averaging segmentation probabilities. SSD [9] and MS-CNN [13] predict objects at different layers of the feature hierarchy. Another category of approaches, including HyperNet [14], ION [15], PVANET [16], and FPN [17],combine outputs from multiple layers to extract more effective features. In fact, it is a critical role to elaborately design the scale of feature maps to recognize objects across multiple scales. Empirically, the context information can conduce to improving the object detection performance in natural scenes. R*CNN [18] building on Fast R-CNN [6] uses more than one region for classification while still maintaining the ability to localize the action. MR-CNN [19] develop a multi-region CNN recognition model that yields an enriched object representation capable to capture a diversity of discriminative appearance factors. [PERSON] et al. [20] designed a novel deformable part-based model that exploits both global and local context around each candidate detection, which can help in detecting objects at all scales especially at tiny objects. As we know, the PASCAL VOC dataset which contains 20 object categories is the most widely used benchmark dataset for generic object detection. The object instances in the PASCAL VOC are usually large because they occupy a major portion of the image. However, the concerned remote sensing object instances such as plane and ship usually have smaller object size in which the difficulty with small objects is intuitive. Current object detectors, like Faster R-CNN, always leverage the convolutional neural networks to extract increasingly abstract feature representations. During this process, the intermediate feature maps are usually down-sampled too many times by the convolutional layer or the pooling layer whose stride is greater than one. Obviously, it is expected that directly applying the Faster R-CNN to detecting the small remote sensing objects only obtains poor performance. To address this issue, we investigate how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. In this paper, we extend the prevailing Faster R-CNN for the small object detection in optical remote sensing images. Firstly, we elaborately modify anchors in the RPN stage of Faster R-CNN based on the statistics of our training set to generate the small object proposals.