Saab gets $768 mln order for defence equipment

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  19 septembre 2018 | International, C4ISR

  Differentiating a port from a shipyard is a new kind of problem for AI

  By: Daniel Cebul It's well known that satellites and other intelligence, surveillance and reconnaissance platforms collect more data than is possible for humans to analyze. To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports. Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN. This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt's team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds. The team's algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations. “Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn't do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don't have any distinguishing features from the point of view of a satellite image for the algorithms to recognize. “Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It's kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said. But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin's algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it's a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.” However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community. Hakjae Kim, IARPA's program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren't able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.” Kim and Pritt stressed managing expectations for CNN-based artificial intelligence. “This is a real technology that will work, but it also has limitations. I don't want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don't want the users in the field to get disappointed by the initial delivery of this technology and say 'Oh, this is another technology that was oversold and this is not something we can use," he added. Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn't really know anything, [it] doesn't have any common sense.'” So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim's team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application," he furthered. Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They've found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU's heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.” But don't expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don't think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly," he concluded. https://www.c4isrnet.com/intel-geoint/2018/09/18/differentiating-a-port-from-a-shipyard-is-a-new-kind-of-problem-for-ai

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  20 juillet 2022 | International, Aérospatial

  Textron Aviation Special Missions introduces Citation Longitude Maritime Patrol Aircraft - Skies Mag

  The Maritime Patrol Aircraft variant of Textron's Cessna Citation Longitude is outfitted with a variety of equipment that makes it suited for surveillance missions over land and water, as well as SAR, border patrol, and fishery monitoring missions.

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  8 janvier 2019 | International, Terrestre, C4ISR

  The Army's M1 Abrams Tank Is About To Get Even Deadlier

  by Kris Osborn The Army is engineering new AI-enabled Hostile Fire Detection sensors for its fleet of armored combat vehicles to identify, track and target incoming enemy small arms fire. This system, integrated onto Apache Attack helicopters, uses infrared sensors to ID a “muzzle flash” or heat signature from an enemy weapon. The location of enemy fire could then be determined by a gateway processor on board the helicopter able to quickly geolocate the attack. The Army is engineering new AI-enabled Hostile Fire Detection sensors for its fleet of armored combat vehicles to identify, track and target incoming enemy small arms fire. Even if the enemy rounds being fired are from small arms fire and not necessarily an urgent or immediate threat to heavily armored combat vehicles such as an Abrams, Stryker or Bradley, there is naturally great value in quickly finding the location of incoming enemy small arms attacks, Army weapons developers explain. There are a range of sensors now being explored by Army developers; infrared sensors, for example, are designed to identify the “heat” signature emerging from enemy fire and, over the years, the Army has also used focal plane array detection technology as well as acoustic sensors. “We are collecting threat signature data and assessing sensors and algorithm performance,” Gene Klager, Deputy Director, Ground Combat Systems Division, Night Vision and Electronic Sensors Directorate, told Warrior Maven in an interview last year. Klager's unit, which works closely with Army acquisition to identify and at times fast-track technology to war, is part of the Army's Communications, Electronics, Research, Development and Engineering Center (CERDEC). Army senior leaders also told Warrior Maven the service will be further integrating HFD sensors this year, in preparation for more formals testing to follow in 2019. Enabling counterattack is a fundamental element of this, because being able to ID enemy fire would enable vehicle crews to attack targets from beneath the protection of an armored hatch. The Army currently deploys a targeting and attack system called Common Remotely Operated Weapons System, or CROWS; using a display screen, targeting sensors and controls operating externally mounted weapons, CROWS enables soldiers to attack from beneath the protection of armor. “If we get a hostile fire detection, the CROWS could be slued to that location to engage what we call slue to cue,” Klager said. Much of the emerging technology tied to these sensors can be understood in the context of artificial intelligence, or AI. Computer automation, using advanced algorithms and various forms of analytics, can quickly process incoming sensor data to ID a hostile fire signature. “AI also takes other information into account and helps reduce false alarms,” Klager explained. AI developers often explain that computer are able to much more efficiently organize information and perform key procedural functions such as performing checklists or identifying points of relevance; however, many of those same experts also add that human cognition, as something uniquely suited to solving dynamic problems and weighing multiple variables in real time, is nonetheless something still indispensable to most combat operations. Over the years, there have been a handful of small arms detection technologies tested and incorporated into helicopters; one of them, which first emerged as something the Army was evaluating in 2010 is called Ground Fire Acquisition System, or GFAS. This system, integrated onto Apache Attack helicopters, uses infrared sensors to ID a “muzzle flash” or heat signature from an enemy weapon. The location of enemy fire could then be determined by a gateway processor on board the helicopter able to quickly geolocate the attack. While Klager said there are, without question, similarities between air-combat HFD technologies and those emerging for ground combat vehicles, he did point to some distinct differences. “From ground to ground, you have a lot more moving objects,” he said. Potential integration between HFD and Active Protection Systems is also part of the calculus, Klager explained. APS technology, now being assessed on Army Abrams tanks, Bradleys and Strykers, uses sensors, fire control technology and interceptors to ID and knock out incoming RPGs and ATGMs, among other things. While APS, in concept and application, involves threats larger or more substantial than things like small arms fire, there is great combat utility in synching APS to HFD. Full article: https://nationalinterest.org/blog/buzz/armys-m1-abrams-tank-about-get-even-deadlier-40847