89 night border monitoring over water and land. As well as having useful vision in the dark, thermal imagers are also very sensitive to daylight movements of water such as wakes and splashes, so they are proving useful in maritime surveys. While commercial operators might not think of themselves as being multirole operators in the way defence users do, many inevitably use their thermal sensors across a similarly broad range of applications. An energy company for instance might perform corridor mapping of stretches of power lines on one day, fields covered in solar panels on another, and a large structure such as a power station on the next. Similarly, an oil & gas company carrying out autonomous pipeline inspections might need a gimbal with both a LWIR sensor for assessing the condition of a pipeline and an MWIR for searching for leaks and identifying any chemicals being leaked. Given the high prices of payload sensors, users will often have only one or a few gimbals that they swap across different vehicles to suit the mission. Inspecting tall structures for instance will benefit from using a multi-rotor UAV that can hover, ascend and descend while maintaining a GNSS position. Long and wide-area surveys are better served by fixed-wing UAVs, but different users will have preferences between, say, a catapult-launched aircraft in need of sensors that can survive the high g-loads and the shock of deployment, or VTOLtransitioning craft that undergoes less shock but needs a lighter gimbal because of the endurance lost during ascent and descent per gram of extra weight. To serve all these users, payload manufacturers work on cutting out weight wherever they can, by using higher strength-to-weight grades of aluminium and magnesium (and in some cases carbon composites) in structural components in the gimbals and thermal camera housings. They design their products as one-size-fits-all solutions in terms of being both light and strong enough for as many vehicles as possible. Also, in seeking to cover the different applications and the gathering of different data types at once, multi-spectral imagers are proliferating. As well as the crossover between LWIR and MWIR, combining imagers built for other bands such as NIR (near-infrared) can enable precise temperature readings of very hot surfaces when monitoring solar farms, for example, or detecting bruises or other damage to farm crops. And while such combinations enable the latest advances in IR sensors to be exploited, arguably most of the more dynamic and important changes in thermal imaging are coming from the accelerating use of edge computing and AI technologies to enhance the value of data gathered from thermal monitoring and inspection tasks. AI in maritime thermography for instance can distinguish between people, marine creatures and different kinds of boats with high fidelity. Alternatively, in wildfire monitoring, AI can produce geo-referenced maps of fire outlines or hotspots, with thermal imagers able to see where fires are still burning under trees or smoke, or otherwise hidden from the human eye. Combining thermal imagers with NDVI (normalised difference vegetation index) sensors can also allow flooding to be mapped autonomously, detecting and drawing lines around the perimeters of where water is present. Autonomous vehicle applications present a valuable motivator for thermal sensor manufacturers to enhance their AI offerings. In principle, a machine can now be trained to recognise Thermal imaging | Focus Given the high price of payload sensors, users will often have only one or a few gimbals that they swap across different vehicles to suit the mission Uncrewed Systems Technology | December/January 2024 UAVs increasingly integrate more than one multi-sensor payload, covering multi-spectral surveys in all weathers and visibility conditions (Courtesy of Overwatch Imaging)
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