Ship target detection of unmanned surface vehicle base on efficientdet

Abstract

The autonomous navigation of unmanned surface vehicles (USV) depends mainly on effective ship target detection to the nearby water area. The difficulty of target detection for USV derives from the complexity of the external environment, such as the light reflection and the cloud or mist shield. Accordingly, this paper proposes a target detection technology for USV on the basis of the EfficientDet algorithm. The ship features fusion is performed by Bi-directional Feature Pyra-mid Network (BiFPN), in which the pre-trained EfficientNet via ImageNet is taken as the backbone network, then the detection speed is increased by group normalization. Compared with the Faster-RCNN and Yolo V3, the ship target detection accuracy is greatly improved to 87.5% in complex environments. The algorithm can be applied to the identification of dynamic targets on the sea, which provides a key reference for the autonomous navigation of USV and the military threats assessment on the sea surface.

Keywords

• unmanned surface vehicle(usv)
• efficientdet
• target detection
• group normalization(gn)