Applications of event‐based image sensors—Review and analysis

Leñero‐Bardallo, Juan A.; Carmona-Galán, Ricardo; Rodríguez-Vázquez, Á.

doi:10.1002/cta.2546

Cited by 14 publications

(8 citation statements)

References 52 publications

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“…However, from the point of view of vision-based measurement system research, a low resolution does not limit the use of the event-based neuromorphic vision sensor in autonomous vehicles. The competitiveness of event-based sensors in autonomous vehicles lies in their main features, not high image quality [63]. These main features include low power and bandwidth consumption and the ability to respond to dynamically changing scenarios.…”

Section: Turn-light Measurement Resultsmentioning

confidence: 99%

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Liu

Chen

et al. 2022

Journal of Advanced Transportation

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An important aspect of the perception system for intelligent vehicles is the detection and signal measurement of vehicle taillights. In this work, we present a novel vision-based measurement (VBM) system, using an event-based neuromorphic vision sensor, which is able to detect and measure the vehicle taillight signal robustly. To the best of our knowledge, it is for the first time the neuromorphic vision sensor is paid attention to for utilizing in the field of vehicle taillight signal measurement. The event-based neuromorphic vision sensor is a bioinspired sensor that records pixel-level intensity changes, called events, as well as the whole picture of the scene. The events naturally respond to illumination changes (such as the ON and OFF state of taillights) in the scene with very low latency. Moreover, the property of a higher dynamic range increases the sensor sensitivity and performance in poor lighting conditions. In this paper, we consider an event-driven solution to measure vehicle taillight signals. In contrast to most existing work that relies purely on standard frame-based cameras for the taillight signal measurement, the presented mixed event/frame system extracts the frequency domain features from the spatial and temporal signal of each taillight region and measures the taillight signal by combining the active-pixel sensor (APS) frames and dynamic vision sensor (DVS) events. A thresholding algorithm and a learned classifier are proposed to jointly achieve the brake-light and turn-light signal measurement. Experiments with real traffic scenes demonstrate the performance of measuring taillight signals under different traffic conditions with a single event-based neuromorphic vision sensor. The results show the high potential of the event-based neuromorphic vision sensor being used for optical signal measurement applications, especially in dynamic environments.

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Section: Turn-light Measurement Resultsmentioning

confidence: 99%

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Liu

Chen

et al. 2022

Journal of Advanced Transportation

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“…Because of the excellent performance of PdSe 2 in the field of photodetection, some studies subsequently explored further possibilities in the image sensor field. Infrared image sensors have emerged as an essential device unit in optoelectronic systems such as fire monitoring, night vision, and surveillance cameras [ 118 ].…”

Section: Pdse 2 -Based Van Der Waals Heterostructuresmentioning

confidence: 99%

Applications of 2D-Layered Palladium Diselenide and Its van der Waals Heterostructures in Electronics and Optoelectronics

et al. 2021

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The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe2) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe2. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe2, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe2 nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe2 and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe2 van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community.

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“…Their main advantages over frame based sensors are their fast response time and important dynamic range [107]. Detailed information on their implementations and working principles can be found in [105,111].…”

Section: Event Drivenmentioning

confidence: 99%

Spiking Neural Networks Hardware Implementations and Challenges

Bouvier

Valentian

Mesquida

et al. 2019

J. Emerg. Technol. Comput. Syst.

179

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Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.

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Applications of event‐based image sensors—Review and analysis

Cited by 14 publications

References 52 publications

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Applications of 2D-Layered Palladium Diselenide and Its van der Waals Heterostructures in Electronics and Optoelectronics

Spiking Neural Networks Hardware Implementations and Challenges

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