Adaptive museum lighting using CNN-based image segmentation

Cerpentier, Jeroen; Rondelez, Nick; Slembrouck, Nathan; Gielen, Stef; Acuña, Paula; Meuret, Youri

doi:10.1016/j.buildenv.2023.110552

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…This optical system is then combined with * e-mail: jeroen.cerpentier@kuleuven.be a computer vision algorithm, trained to detect certain target objects. After the detection, a feedback loop is deployed to match the resulting light spot of the adaptive luminaire with the detected object [11].…”

Section: Introductionmentioning

confidence: 99%

Smart lighting system with tunable radiation pattern

Cerpentier,

Rondelez,

Slembrouck

et al. 2023

EPJ Web Conf.

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Smart lighting systems are capable of producing light when and where it is needed. Such functionality can be achieved with adaptive optical systems, which consist of one or multiple adjustable components, enabling illumination with a variable radiation pattern. This paper introduces the design of a compact, tunable optical system, allowing illumination with variable beam size and beam direction. We demonstrate how this system can be combined with computer vision and a feedback loop, to achieve a fully autonomous, smart illumination system.

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Section: Introductionmentioning

confidence: 99%

Smart lighting system with tunable radiation pattern

Cerpentier,

Rondelez,

Slembrouck

et al. 2023

EPJ Web Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This limitation can be addressed with adaptive light beams, using tunable lenses and/or adjustable mirrors [7,8]. Despite their potential, adaptive camera-based smart lighting systems are not yet commercialized for general lighting applications, and the research of such systems is still in an early stage [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Controlling the target pattern of projected LED arrays for smart lighting

Cerpentier,

Meuret,

Acuña

2023

Preprint

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High-resolution, pixelated LED arrays allow flexible illumination. By addressing certain areas of the LED matrix and projecting the emitted light, selective illumination can be achieved. When combined with computer vision, smart, autonomous lighting systems are within reach. However, limitations of the used projection optics, in combination with the fact that the LED array and camera can be at a different position, severely complicates the problem of calculating which LED pixels to address in order to achieve a desired target pattern. This work proposes a least-squares deconvolution-based calculation method to solve this problem. The method relies on an initial calibration step that characterizes the complete point-spread-function of the LED array for the considered illumination configuration. This allows using the system for various settings. The method is experimentally validated for an off-axis illumination configuration that demonstrates the accuracy and flexibility of the approach. Because the proposed algorithm is fast and guarantees a global optimum, it opens new avenues towards accurate, smart and adaptive illumination.

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