Event-based sensors (EBSs) consist of a pixelated focal plane array in which each pixel is an independent asynchronous change detector. The analog asynchronous array is read by a synchronous digital readout and written to disk. As a result, EBS pixels consume minimal power and bandwidth unless the scene changes. Furthermore, the change detectors have a very large dynamic range (~120 dB) and rapid response time (~20 us). A framing camera with comparable speed requires ~3 orders of magnitude more power and ~2 orders of magnitude higher bandwidth. Remote sensing deployed in the field requires low power, low bandwidth, and low complexity algorithms. An EBS inherently allows for low power and low bandwidth, but there is a lack of mature image analysis algorithms. While analysis of conventional imagers draws from decades of image processing algorithms, EBS data is a fundamentally different format; a series of x, y, asynchronous time, and polarization change (increase/decrease) as opposed to x, y, and intensity at a regularly sampled framerate. Our team has worked to develop and refine image processing algorithms that use EBS data directly.
Event-based sensors (EBS) consist of a pixelated focal plane array in which each pixel is an independent asynchronous change detector. The analog asynchronous array is read by a synchronous digital readout and written to disk. As a result, EBS pixels consume minimal power and bandwidth unless the scene changes. Furthermore, the change detectors have a very large dynamic range (~120 dB) and rapid response time (~20 us). A framing camera with comparable speed requires ~3 orders of magnitude more power and ~2 orders of magnitude higher bandwidth. These features make EBS an appealing technology for proliferation detection applications. Remote sensing deployed in the field requires low power, low bandwidth, and low complexity algorithms. EBS inherently allows for low power and low bandwidth, but a drawback of event-based sensors is the lack of mature image analysis algorithms. While analysis of conventional imagers draws from decades of image processing algorithms, EBS data is a fundamentally different format; a series of x, y, asynchronous time, and polarization change (increase/decrease) as opposed to x, y, and intensity at a regularly sampled framerate. To leverage the advantages of EBS over conventional imagers, our team has worked to develop and refine image processing algorithms that use EBS data directly. We will discuss these efforts, including frequency and phase detection. We will also discuss the field applications of these algorithms such as degraded visual environments (e.g., fog) and defeating laser dazzling attempts.
Event-based sensors are a novel sensing technology which capture the dynamics of a scene via pixel-level change detection. This technology operates with high speed (>10 kHz), low latency (10 μs), low power consumption (<1 W), and high dynamic range (120 dB). Compared to conventional, frame-based architectures that consistently report data for each pixel at a given frame rate, event-based sensor pixels only report data if a change in pixel intensity occurred. This affords the possibility of dramatically reducing the data reported in bandwidth-limited environments (e.g., remote sensing) and thus, the data needed to be processed while still recovering significant events. Degraded visual environments, such as those generated by fog, often hinder situational awareness by decreasing optical resolution and transmission range via random scattering of light. To respond to this challenge, we present the deployment of an event-based sensor in a controlled, experimentally generated, well-characterized degraded visual environment (a fog analogue), for detection of a modulated signal and comparison of data collected from an event-based sensor and from a traditional framing sensor.
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