Aerospace & Military

Whether your machine vision application is surveying, mapping, land-use planning, archaeology, movie production, environmental studies, power line inspection, surveillance, commercial advertising, conveyancing, construction site assessment, agriculture, or remote sensing - obtaining clear, sharp images from a moving aircraft is one of the biggest challenges you will face. Factors to take into consideration when selecting the appropriate aerial cameras are: Resolution, Sensor Size, Sensitivity, Noise, Shutter Type, Dynamic Range, Bit Depth Size, Weight, and Power. Our engineers can recommend the best industrial cameras to best meet the needs of your specific aerial application. We will work to minimize motion blur while covering as much ground as possible. Our industrial cameras are designed for rugged use and proven repeatable performance day-in and day-out. Our industrial CMOS cameras with are available in various sensor sizes, shutter options and interface options.

We offer cameras specifically designed for Intelligence, Surveillance & Reconnaissance (ISR). Designed specifically for aerial imaging, and offering advanced features such as extended operating range and rugged reliability. Small, lightweight and affordable which make them ideal for airborne platforms. The ability to capture images from air, land, or sea is easily accomplished using our ultra-high resoltuion cameras supporting GigE, Coaxpress, HD-SDI or Cameralink interfaces.

Machine vision cameras are used in ordinance testing, offering precise and efficient methods for evaluating the performance and reliability of military weapons and munitions. These systems utilize high-speed cameras, specialized optics, and advanced image processing algorithms to capture and analyze the behavior of projectiles, explosives, or other ordinance components during test firings or detonations. By recording and analyzing high-resolution video footage of test events, machine vision systems can accurately measure parameters such as velocity, trajectory, impact force, and fragmentation patterns. This data provides valuable insights into the effectiveness and safety of ordinance systems, helping military researchers and engineers assess performance, identify potential defects or malfunctions, and optimize design parameters. Additionally, machine vision systems enhance safety by enabling remote monitoring and analysis of test events, minimizing the need for personnel to be in close proximity to hazardous test environments. With their ability to provide detailed, objective, and real-time feedback, machine vision systems play a critical role in ensuring the reliability and effectiveness of ordinance systems for military applications.

Machine vision cameras are used in unmanned aerial vehicles (UAVs), also known as drones, enabling a wide range of applications in surveillance, reconnaissance, mapping, and remote sensing. These cameras, often equipped with high-resolution sensors and stabilized gimbals, capture high-quality images and videos from aerial perspectives. In UAVs, machine vision cameras provide real-time visual feedback to operators, allowing for precise navigation, target identification, and situational awareness during missions. Additionally, machine vision algorithms can analyze the captured imagery to extract valuable information such as terrain features, object detection, or environmental changes. This data is crucial for various applications, including agriculture (crop monitoring, pest detection), infrastructure inspection (power lines, pipelines), environmental monitoring (wildlife tracking, pollution detection), and disaster response (search and rescue, damage assessment). With advancements in artificial intelligence and autonomous navigation, machine vision cameras in UAVs can also enable automated flight paths, obstacle avoidance, and intelligent decision-making, further enhancing their capabilities for diverse aerial tasks. Overall, machine vision cameras play a pivotal role in unlocking the full potential of UAVs for a wide range of civilian and military applications, driving innovation and efficiency in aerial operations.

Machine vision cameras are used in mobile mapping systems, facilitating the collection of detailed geospatial data for various applications such as urban planning, infrastructure management, and navigation. Mounted on vehicles, drones, or handheld devices, these cameras capture high-resolution images and videos of the surrounding environment as they traverse different locations. Equipped with advanced sensors and optics, machine vision cameras can accurately capture features such as roads, buildings, vegetation, and terrain with precision and clarity. In mobile mapping systems, these cameras work in conjunction with other sensors such as GPS, LiDAR, and inertial measurement units (IMUs) to geo-reference the captured imagery and create accurate 3D models or maps of the area. Machine vision algorithms analyze the captured data to extract valuable information such as object detection, road markings, or land use classification. This data is used for various purposes including urban planning, disaster management, autonomous navigation, and virtual reality applications. By providing comprehensive and up-to-date spatial information, machine vision cameras enable efficient decision-making and resource allocation in a wide range of industries and sectors.

Machine vision cameras are used in remote sensing systems, capturing high-resolution images and data from Earth's surface, atmosphere, and oceans to monitor environmental changes, manage natural resources, and study Earth's systems. These cameras, often mounted on satellites, aircraft, drones, or ground-based platforms, utilize advanced sensors and optics to capture multispectral or hyperspectral imagery across various wavelengths of light. By analyzing the captured data, machine vision cameras can provide valuable insights into vegetation health, land use and land cover changes, soil moisture content, ocean color, atmospheric composition, and more. Remote sensing systems equipped with machine vision cameras enable scientists, researchers, and policymakers to monitor and manage ecosystems, assess the impacts of climate change, predict natural disasters, and support sustainable resource management practices. Additionally, machine learning algorithms can be applied to analyze and interpret remote sensing data, further enhancing the accuracy and efficiency of environmental monitoring and assessment efforts. Overall, machine vision cameras are indispensable tools in remote sensing systems, empowering stakeholders with valuable information to make informed decisions and address environmental challenges on a global scale.