Robust object detection for tiny and dense targets in VHR aerial images
Abstract
Object detection in Very High Resolution (VHR) optical remote sensing images is a challenged work for objects are usually dense and tiny. With random orientation, various backgrounds as well as unpredictable noise make traditional image processing methods perform badly. In this paper, we propose using state-of-art Region-based fully convolutional networks to solve object detection tasks in aerial images. To make the whole system efficient we choose to utilize position-sensitive score maps which not only fully take advantage of the convolutional feature maps but also achieve a balance between translation-variance in object detection and translation-invariance in classification. In addition, with 101-layer Residual networks as feature extractors, we achieve a satisfying result which is low time consuming and shows 99.45 percent and 94.41 percent precision respectively on two datasets.