Anchor-free network with guided attention for ship detection in aerial imagery

For their high maneuverability, unmanned aerial vehicles (UAVs) are widely used in object detection, including the detection of ships. However, object detection in aerial images taken by UAV remains a challenge due to the arbitrary shooting perspectives and small proportion of targets. Existing anchor-based detectors, whose performance could be easily affected by the aspect ratios and scales of anchor boxes, could get into difficulties in handling candidate targets with wide shape variations. We propose an efficient anchor-free detector to replace a set of predefined anchor boxes. Specifically, guided attention module, embedded in the feature pyramid structure, is put forward to help low-level feature maps acquire the guiding information of high-level feature maps in the multi-scale fusion stage. Then an intersection-over-union (IoU) prediction head is added to predict the IoU for each predicted box. The output from IoU prediction and classification branches is then evaluated to dynamically generate soft labels without sacrificing the effiency in an attempt to improve the performance of the proposed detector. The results of extensive experiments demonstrate that the performance of our proposed detector is better than that of several current mainstream detectors.