Accurate identification of ships is the key technology of intelligent transportation in water.At the same time, it also provides a judgment basis for water traffic Pentair SunTouch safety control.This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3).
The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background.For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement Made in the USA Dog Treats module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network.Experiments were carried out on the public SeaShips dataset.
Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.