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Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.

EDITH MAERSK: The largest ever ship on the Thames arrived.

The installation of the gates for the new locks of the Panama Canal successfully began today on the Atlantic side of the waterway.

De mystérieuses lumières vertes observées en Thaïlande depuis l'espace.

India Introduces Biometric Facial ID for Seafarers.