Assessing High Dynamic Range Imagery Performance for Object Detection in Maritime Environments

The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. Faster-RCNN, SSD, and YOLOv5 were used to compare. Results determined Faster-RCNN and YOLOv5 networks trained on fixed exposure images outperformed their HDR counterparts while SSDs performed better when using HDR images. Better fixed exposure network performance is likely attributed to better feature extraction for fixed exposure images. Despite performance metrics, HDR images prove more beneficial in cases of extreme light exposure since features are not lost

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Enhancing automatic maritime surveillance systems with visual information

Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing Vessel Traffic Services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, collected from multiple real sites, with different light, weather, and traffic conditions

Light environment - A. Visible light. B. Ultraviolet light

Visible and ultraviolet light environment as related to human performance and safety during space mission

A Cross-Disciplinary Approach to the Maritime Security Risk of Piracy and Lessons Learned From Agent-Based Modeling

This dissertation takes a cross-disciplinary approach to understanding pirate activity. Maritime piracy presents a dynamic ever-evolving problem. In today’s globalized world, contemporary maritime piracy presents a transnational threat. It is a complex socio-economic and political problem which the modern world considers to be criminal activity. Like all complex problems it must be deconstructed to fully comprehend it. All criminal activity, maritime piracy included, has certain elements of supply and demand. For the activity to occur there must be a certain level, or supply, of targets. At the same time, we can posit that there must be a lack of other opportunities for the pirates, who calculate that the risk of engaging in piracy is worthwhile. This risk calculation is a function of the potential rewards minus the sum of the risks. An increase in pirate attacks creates a demand for better maritime security. An increase in maritime security causes an increase in risk to pirates. Improved pirate capabilities may decrease this risk. The result is a constantly evolving complex problem. This study proposes a parsimonious agent-based model, focused on the socio-economic and political variables that encourage piracy, with utility across many specific regional domains. By simplifying the details of certain aspects of the model, the focus is placed on the issues at the heart of the problem. This allows for new insights into the dynamic relationship between these factors

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 253 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1975

Multitemporal Very High Resolution from Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper

Maritime threat response

This report was prepared by Systems Engineering and Analysis Cohort Nine (SEA-9) Maritime Threat Response, (MTR) team members.Background: The 2006 Naval Postgraduate School (NPS) Cross-Campus Integrated Study, titled “Maritime Threat Response” involved the combined effort of 7 NPS Systems Engineering students, 7 Singaporean Temasek Defense Systems Institute (TDSI) students, 12 students from the Total Ship Systems Engineering (TSSE) curriculum, and numerous NPS faculty members from different NPS departments. After receiving tasking provided by the Wayne E. Meyer Institute of Systems Engineering at NPS in support of the Office of the Assistant Secretary of Defense for Homeland Defense, the study examined ways to validate intelligence and respond to maritime terrorist attacks against United States coastal harbors and ports. Through assessment of likely harbors and waterways to base the study upon, the San Francisco Bay was selected as a representative test-bed for the integrated study. The NPS Systems Engineering and Analysis Cohort 9 (SEA-9) Maritime Threat Response (MTR) team, in conjunction with the TDSI students, used the Systems Engineering Lifecycle Process (SELP) [shown in Figure ES-1, p. xxiii ] as a systems engineering framework to conduct the multi-disciplinary study. While not actually fabricating any hardware, such a process was well-suited for tailoring to the team’s research efforts and project focus. The SELP was an iterative process used to bound and scope the MTR problem, determine needs, requirements, functions, and to design architecture alternatives to satisfy stakeholder needs and desires. The SoS approach taken [shown in Figure ES-2, p. xxiv ]enabled the team to apply a systematic approach to problem definition, needs analysis, requirements, analysis, functional analysis, and then architecture development and assessment.In the twenty-first century, the threat of asymmetric warfare in the form of terrorism is one of the most likely direct threats to the United States homeland. It has been recognized that perhaps the key element in protecting the continental United States from terrorist threats is obtaining intelligence of impending attacks in advance. Enormous amounts of resources are currently allocated to obtaining and parsing such intelligence. However, it remains a difficult problem to deal with such attacks once intelligence is obtained. In this context, the Maritime Threat Response Project has applied Systems Engineering processes to propose different cost-effective System of Systems (SoS) architecture solutions to surface-based terrorist threats emanating from the maritime domain. The project applied a five-year time horizon to provide near-term solutions to the prospective decision makers and take maximum advantage of commercial off-the-shelf (COTS) solutions and emphasize new Concepts of Operations (CONOPS) for existing systems. Results provided insight into requirements for interagency interactions in support of Maritime Security and demonstrated the criticality of timely and accurate intelligence in support of counterterror operations.This report was prepared for the Office of the Assistant Secretary of Defense for Homeland DefenseApproved for public release; distribution is unlimited

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 156)

This bibliography lists 170 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network