An Aggregation Context Network for Detecting Small Ships in Remote Sensing Images

Small ship detection in remote sensing images is an increasingly important area in computer vision. The main greatest challenges that face it are a small size and different aspect ratios of ships. Recently, convolution neural network (CNN) achieve outstanding performance in object detection. CNN consists of several consecutive layers categorized to shallow and deep according to their positions. The shallow layers contribute in the detection of tiny targets significantly. However, the semantic features of these layers are weak to classify them correctly. In this paper, an aggregation context network is proposed to enhance the semantic features of the shallow layers for the state-of-the-art RetinaNet detector. This network is located before the feature pyramid network of the RetinaNet. It consists of aggregation and context modules. The aggregation module aggregates the different features to improve the semantic features at the shallow layers. The context module is to increase the receptive fields at each layer of the feature pyramid network. The experiments are carried on a proposed ship dataset. The dataset is carefully picked up where most of its instances relative area is less than 1% of the input image. The proposed network achieves 89.39%, which outperforms other state-of-the-art detectors.