Abstract: Ships in remote sensing images are arranged in arbitrary directions.The general target detection algorithm based on deep learning use horizontal bounding box to locate object,which will select a large number of backgrounds when detecting ships.The ships detection performance based on general object detection method is not good.An improved ships detection algorithm based on fully convolutional one-stage(FCOS) object detection network is proposed.Taking FCOS as the baseline,an offset regression branch is added to detection head,and a rotating bounding box is generated by shifting the upper midpoint and the right midpoint of the horizontal bounding box.The ships usually have high aspect ratio,and the angle deviation between the predicted bounding box and the real bounding box has a great influence on the intersection over union(IoU),which damages the detection accuracy of the model.In order to solve this problem,a weighting factor related to the aspect ratio of ships is introduced to calculate the offset loss,so that the target with a large aspect ratio can obtain relatively large offset loss.The proposed method and several mainstream rotating target detection algorithms are tested on HRSC2016 dataset.The results show that the average accuracy of the proposed method is 89.00% and the detection speed is 19.8FPS.Compared with the same type of algorithms without anchor,the proposed method has superior detection speed and accuracy.

Key words: Remote sensing image, Ships detection, FCOS, Anchor-free algorithm, Offset branch