HFL-Net: Highlight Foreground and Local Scale Features Network for Cross-Domain Ship Detection

Ship detection receives increasing concerns as an essential ocean mission. Recently, deep learning (DL) has greatly improved ship detection accuracy from traditional methods. However, most of exist methods only utilize single source data, there are still some challenges that influence the ship detection performance. Therefore, we propose a highlight foreground and local scale features network (HFL-Net) for cross-domain ship detection to solve the problems of complex inshore background interferences and multi-scale ship feature differences. It is evaluated on two datasets with three other state-of-the-art methods and the experimental results confirm the superiority ofHFL-Net.