Visual Recognition Based on Deep Learning for Navigation Mark Classification

Recognizing objects from camera images is an important field for researching smart ships and intelligent navigation. In sea transportation, navigation marks indicating the features of navigational environments (e.g. channels, special areas, wrecks, etc.) are focused in this paper. A fine-grained classification model named RMA (ResNet-Multiscale-Attention) based on deep learning is proposed to analyse the subtle and local differences among navigation mark types for the recognition of navigation marks. In the RMA model, an attention mechanism based on the fusion of feature maps with three scales is proposed to locate attention regions and capture discriminative characters that are important to distinguish the slight differences among similar navigation marks. Experimental results on a dataset with 10260 navigation mark images showed that the RMA has an accuracy about 96 & x0025; to classify 42 types of navigation marks, and the RMA is better than ResNet-50 model with which the accuracy is about 94 & x0025;. The visualization analyses showed that the RMA model can extract the attention regions and the characters of navigation marks.