Maritime Ship Targets Recognition with Deep Learning
Huixuan Fu, Yuan Li, Yuchao Wang, Peng Li · 2018
In order to improve the accuracy of intelligent recognition of the marine ships besides the realization of the algorithm, a deep learning technique is introduced applying Faster RCNN algorithm to the recognition of marine vessels. The marine target recognition system structure based on Faster RCNN is established. The effectiveness of Faster RCNN algorithm is verified on the VOC2007 dataset, and the marine vessel target recognition experiment is carried out. On the basis, the target database of ships in different marine environments is established and marked, and the new dataset is used to train Faster RCNN network model, the new trained Faster RCNN network model is compared with the old one by taking the marine ship target recognition experiments. The experimental results show that the Faster RCNN model trained on the new created dataset has good recognition ability and robustness for the targets of marine ships while it can be more accurate giving the target area with the low missed detection rate.