LAUSR

Ship detection with several military and civilian applications has drawn considerable attention in recent years. In remote sensing images, ships have the characteristics of arbitrary orientation. Based on the characteristics, many arbitrary-oriented ship detectors have been proposed. Most of these detectors preset many horizontal or rotated anchors and determine the positive and negative samples based on the intersection over union (IoU) between the anchor and ground-truth bounding box, in what is called the label assignment process. However, IoU performance is limited as it can only reflect the quality of the anchor to a certain extent. In addition, the manually fixed IoU threshold to separate the positive and negative limits the flexibility of the method, as different ships may have different optimal thresholds. Moreover, the equally weighted training samples cause a misalignment between the classification and regression heads. Therefore, we propose a dynamic soft label assignment method for arbitrary-oriented ship detection. First, we design a novel anchor quality score function that takes into account both prior and prediction information of the anchor and enables the model to participate in the label assignment process. Second, we propose a dynamic anchor quality score threshold instead of a fixed IoU threshold for dividing positive and negative samples. Third, in contrast to assigning equal weights, we propose a soft label assignment strategy to weigh the training samples in the loss function. The proposed method offers superior detection performances for arbitrary-oriented ships with only one horizontal preset anchor. Experimental results on HRSC2016, FGSD, and ShipRSImageNet datasets demonstrate the effectiveness of our proposed dynamic soft label assignment for arbitrary-oriented ship detection.