Cobra Aero GROUP 3 UAS PROPULSION SYSTEM High Power Density Minimal Maintenance Lightweight Fuel Efficient Designed and developed to aero-space standards Power: 6.5kW (8.7 hp) at 7000 RPM Starting Heat: 300 Watts for 10 min. 410g/kW-hr (0.67 lb/hp-hr) cruise VP P4F heavy fuel or VP C9 50:1 premix, JP5, JP8 Low Acoustic signature 500W 3-PHASE GENERATOR OUTPUT Power4Flight Intelliject EFI system Telemetry and control over Serial or CAN Isolation mount included Liquid cooled with active temp control Quickly Integrate into any airframe COBRA AERO A99H LONG-RANGE HEAVY FUEL PROPULSION SYSYEM For more information visit Cobra-aero.com/A99h Cobra aero Info@cobra-aero.com (517) 437-9100 Scorpion Power System Ltd Scorpion Performance www.scorpionsystem.com POWER SYSTEM OF CHAMPIONS FOR 16+ YEARS MORE PAYLOAD MORE FLIGHT TIME MORE POWER IM-SERIES Multirotor 5°-30° Prop A-Series Airplane / VTOL 10cc-150cc Gas Equivalent HK-Series Helicopter 200mm-1000mm Blade Size ESC’S 6 Step / FOC 30A – 300A Thermal imaging | Focus them than EO and MWIR systems. Developing and training such algorithms through machine learning is expensive, and can take years, but once developed they can be used across different cameras, vehicles and missions to great effect. They offer high rates of success in terms of accurately recognising and tracking different objects, throughout countless possible permutations of thermal patterns, shapes, distances and so on. As with visual image-based detection, classification and tracking, machine learning for thermal imagery starts with a neural network, which is the framework of an algorithm that can be trained into an application-specific model. They include air-to-ground applications such as security forces monitoring road traffic for a particular vehicle type, or maritime scanning for boats engaged in illegal activities or people who’ve fallen overboard. They also include driverless mobility applications such as autonomous braking when they detect and recognise the thermal signatures of pedestrians or animals on the road ahead. After selecting a neural network, training data must be fed into it. This data is often difficult to acquire, especially for thermal AI models. A few datasets are available as open source downloads, with potentially millions of thermal images to train networks in recognising and extracting features from application-specific targets. However, the quality of one image dataset versus another’s can be a huge differentiator in the consistency and accuracy with which object recognition, classification and tracking is performed. The uncrewed industry’s leading providers of intelligent thermal imaging solutions therefore generally began their AI development programmes years before the datasets existed, so they have trained their models on unique ones tailored to mission-specific use cases. Building up such a dataset can be lengthy and expensive. For instance, to train a UAV gimbal to detect and classify any type of vehicle or lifeform on the ground while in flight can require taking millions of images from the air – not just in LWIR and MWIR, but also in EO, as the latter can help confirm correct classifications by the model during development. As most users will use EO and IR simultaneously, it is good practice to train AI to use both at once and in equal measure. The result of all this should be several million annotations – images with bounding boxes or polygons around the target inside each image, with a machine-readable label indicating exactly what the target is. A company with such a dataset can search for groups of features such as annotations of cracks or dents in metal structures, and rapidly train a blank neural network into a C M Y CM MY CY CMY K AD for Magazine 2023_Final.pdf 1 31/10/2023 11:14:26
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