An Image-Based Ship Detector With Deep Learning Algorithms

Abstract

This article provides a comprehensive understanding of the image-based ship detector using computer vision technologies with deep learning. Several pre-trained object detection models, such as MobileNet, VGGNet, Inception, and ResNet, have been investigated by illustrating the network architectures. A group of pre-trained models has been proposed and examined by recognizing ships on the sea and in the bay area. The model testing and comparison procedure have also been performed by evaluating the performance matrix and comparing predictive results per model. The optimal model is then chosen with the additional tests in terms of capabilities of the ship detection using the satellite image streaming in the real world. Such a proposed ship detector can contribute to the development of smart ship operations and may further carve out the possibility for the automated shipping system with smart port management.