A Cascade Rotated Anchor-Aided Detector for Ship Detection in Remote Sensing Images

Automatic ship detection in high-resolution remote sensing images has attracted increasing research interest due to its numerous practical applications. However, there still exist challenges when directly applying state-of-the-art object detection methods to real ship detection, which greatly limits the detection accuracy. In this article, we propose a novel cascade rotated anchor-aided detection network to achieve high-precision performance for detecting arbitrary-oriented ships. First, we develop a data preprocessing embedded cascade structure to reduce large amounts of false positives generated on blank areas in large-size remote sensing images. Second, to achieve accurate arbitrary-oriented ship detection, we design a rotated anchor-aided detection module. This detection module adopts a coarse-to-fine architecture with a cascade refinement module (CRM) to refine the rotated boxes progressively. Meanwhile, it utilizes an anchor-aided strategy similar to anchor-free, thus breaking through the bottlenecks of anchor-based methods and leading to a more flexible detection manner. Besides, a rotated align convolution layer is introduced in CRM to extract features from rotated regions accurately. Experimental results on the challenging DOTA and HRSC2016 data sets show that the proposed method achieves 84.12% and 90.79% AP, respectively, outperforming other state-of-the-art methods.

Automated summary

This article proposes a novel cascade rotated anchor-aided detection network for high-precision performance in detecting arbitrary-oriented ships in high-resolution remote sensing images. The network overcomes the limitations of state-of-the-art object detection methods applied directly to real ship detection through data preprocessing and a rotated anchor-aided detection module. Experimental results on challenging datasets demonstrate that the proposed method outperforms other methods.