文献类型：期刊文献

英文题名：Ship target detection of unmanned surface vehicle base on efficientdet

作者：Li, Ronghui[1];Wu, Jinshan[2];Cao, Liang[1]

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang, Peoples R China

年份：2022

卷号：10

期号：1

起止页码：264

外文期刊名：SYSTEMS SCIENCE & CONTROL ENGINEERING

收录：EI(收录号：20214311048277)、ESCI(收录号：WOS:000784394700001)、Scopus(收录号：2-s2.0-85117337414)、WOS

基金：This work is supported partially by the National Natural Science Foundation of China [grand number 52171346], the Natural Science Foundation of Guangdong Province [grand number 2021A1515012618], the special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province [grand number 2019KZDZX1035], and program for scientific research start-up funds of Guangdong Ocean University.

语种：英文

外文关键词：Unmanned surface vehicle(USV); efficientdet; target detection; group normalization(GN)

外文摘要：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.