Construction Technologies Institute, National Research Council of Italy (ITC-CNR), Via Lombardia, 49, 20098 San Giuliano Milanese, MI, Italy.
Dipartimento di Architettura e Disegno Industriale, Università degli Studi Della Campania "Luigi Vanvitelli", Via San Lorenzo, 81031 Aversa, CE, Italy.
Sensors (Basel). 2022 Oct 11;22(20):7706. doi: 10.3390/s22207706.
Ubiquitous computing has enabled the proliferation of low-cost solutions for capturing information about the user's environment or biometric parameters. In this sense, the do-it-yourself (DIY) approach to build new low-cost systems or verify the correspondence of low-cost systems compared to professional devices allows the spread of application possibilities. Following this trend, the authors aim to present a complete DIY and replicable procedure to evaluate the performance of a low-cost video luminance meter consisting of a Raspberry Pi and a camera module. The method initially consists of designing and developing a LED panel and a light cube that serves as reference illuminance sources. The luminance distribution along the two reference light sources is determined using a Konica Minolta luminance meter. With this approach, it is possible to identify an area for each light source with an almost equal luminance value. By applying a frame that covers part of the panel and shows only the area with nearly homogeneous luminance values and applying the two systems in a dark space in front of the low-cost video luminance meter mounted on a professional reference camera photometer LMK mobile air, it is possible to check the discrepancy in luminance values between the low-cost and professional systems when pointing different homogeneous light sources. In doing so, we primarily consider the peripheral shading effect, better known as the vignetting effect. We then differentiate the correction factor S of the Radiance Pcomb function to better match the luminance values of the low-cost system to the professional device. We also introduce an algorithm to differentiate the S factor depending on the light source. In general, the DIY calibration process described in the paper is time-consuming. However, the subsequent applications in various real-life scenarios allow us to verify the satisfactory performance of the low-cost system in terms of luminance mapping and glare evaluation compared to a professional device.
无处不在的计算使得低成本解决方案能够广泛用于获取用户环境或生物识别参数的信息。从这个意义上说，自行构建新的低成本系统或验证低成本系统与专业设备之间的对应关系的 DIY 方法允许应用可能性的扩展。顺应这一趋势，作者旨在提出一种完整的 DIY 和可复制的程序，以评估由 Raspberry Pi 和相机模块组成的低成本视频亮度计的性能。该方法最初包括设计和开发一个 LED 面板和一个立方光，作为参考照度源。使用 Konica Minolta 亮度计确定两个参考光源的亮度分布。通过这种方法，可以为每个光源找到一个亮度值几乎相等的区域。通过应用一个覆盖面板部分的框架，并仅显示具有几乎均匀亮度值的区域，然后将两个系统应用于低成本视频亮度计安装在专业参考相机光度计 LMK mobile air 前的暗室中，可以检查指向不同均匀光源时低成本和专业系统之间的亮度值差异。在这样做时，我们主要考虑周边阴影效应，通常称为渐晕效应。然后，我们区分亮度 Pcomb 函数的校正因子 S，以更好地将低成本系统的亮度值与专业设备匹配。我们还引入了一种根据光源来区分 S 因子的算法。总的来说，本文中描述的 DIY 校准过程很耗时。但是，在各种实际场景中的后续应用允许我们验证低成本系统在亮度映射和眩光评估方面与专业设备相比的令人满意的性能。
