A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset

The rapid detection of ships within the wide sea area is essential for intelligence acquisition. Most modern deep learning-based ship detection methods focus on locating ships in high-resolution (HR) remote sensing (RS) images. Seldom efforts have been made on ship detection in medium-resolution (MR) RS images. An MR image covers a much wider area than an HR one of the same size, thus facilitating quick ship detection. To this end, we propose a tiny ship detection method namely, Degraded Reconstruction Enhancement Network (DRENet), for MR RS images. Different from previous methods that mainly focus on feature fusion strategies to improve the expression ability of the detector, we design an additional network branch, i.e., degraded reconstruction enhancer, to learn to regress an object-aware blurred version of the input image in the training phase. Our intuition is that the proposed reconstruction branch may guide the backbone to focus more on tiny ship targets instead of the vast background. Moreover, we incorporate a CRoss-stage Multi-head Attention module in the detector to further improve the feature discrimination by leveraging the self-attention mechanism. To fill the gap of lacking a large-scale MR ship detection dataset, we introduce Levir-Ship, which contains 3876 GF-1/GF-6 multi-spectral images and over 3K tiny ship instances. Experiments on Levir-Ship validate the effectiveness and efficiency of the proposed method. Our method achieves 82.4 AP with 85 FPS, which outperforms many state-of-the-art ship detection methods. Our code and dataset will be made public.