Pixel-Wise Ship Identification From Maritime Images via a Semantic Segmentation Model

Abstract

Accurately identifying ships from maritime surveillance videos attracts increasing attention in the smart ship community, considering that the videos provide informative yet easily understandable spatial–temporal traffic information for varied maritime traffic participants. Previous studies (e.g., ship detection and ship tracking) are conducted by learning distinct features from training samples labeled in terms of bounding boxes, and thus, background pixels may be wrongly trained as ship features. To bridge the gap, we propose a novel approach for achieving a pixel-wise ship segmentation and identification task through a novel design of U-Net deep learning architecture (denoted as EU-Net). The encoder of the EU-Net extracts distinct ship features from input maritime images, and its decoder outputs ship segmentation results in the pixel-wise manner. The proposed EU-Net model consists of encoder and decoder parts via the help of a convolution layer, a depth separable convolution layer, a softmax layer, and so on. More specifically, the EU-Net model identifies each pixel into ship or non-ship as the final output. Experimental results suggest that our proposed model can accurately identify ship (in terms of pixels), considering that the ship segmentation accuracies were larger than 99%. The proposed ship segmentation framework can be further adaptively deployed in the ship sensing system for maritime traffic situation awareness and intelligent visual navigation in a smart ship era.