Ship Detection in Large Scale Sar Images Based on Bias Classification
Xiaoya Wang, Zongyong Cui, Zongjie Cao, Yu Tian · 2020
With the development of imaging technology, ship target detection in large scenes has become a research hotspot. The patches without sea area sent to detector greatly increase the computational cost and there are many false alarms in land area. Based on above, this paper proposes a ship detection method based on bias classification. Patches of large scale SAR images without sea area will no longer be sent to the detector, which greatly reduces false alarms in land area. The proposed method is implemented in the anchor-free network framework named CenterNet. The experimental results show that the bias classification method proposed in this paper can effectively reduce false alarms in land area.