Object Detection Using Convolutional Neural Networks in a Coarse-to-Fine Manner

Object detection in remote sensing images has long been studied, but it remains challenging due to the diversity of objects and the complexity of backgrounds. In this letter, we propose an object detection method using convolutional neural networks (CNNs) in a coarse-to-fine manner. In the coarse step, coarse candidate regions that may contain objects are proposed. In the fine step, fine candidate regions are cropped from coarse candidate regions, and are classified as objects or backgrounds. We design a concise and efficient framework that can propose fewer candidate regions and extract more discriminative features. The framework consists of two eight-layer CNNs that are well designed and powerful. To use CNNs to detect inshore ships, image samples are required, each of which should contain only one ship. However, the traditional image cropping method cannot generate such samples. To solve this problem, we present an orientation-free image cropping method that can generate trapezium rather than rectangle samples, making inshore ship detection by CNN feasible. Experimental results on Google Earth images demonstrate that the proposed method outperforms existing state-of-the-art methods.