Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning.

Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.

Confocal fluorescence microscopy with high-NA diffractive lens arrays.

Traditionally, there is a trade-off between the numerical aperture and field of view for a microscope objective. Diffractive lens arrays (DLAs) with overlapping apertures are used to overcome such a problem. A spot array with an NA up to 0.83 and a pitch of 75 µm is produced by the proposed DLA at a wavelength of 488 nm. By measurement of the fluorescence beads, the DLA-based confocal setup shows the capability of high-resolution measurement over an area of 3mm×3mm with a 2.5×0.07NA objective. Further, the proposed fluorescence microscope is insensitive to optical aberrations, which has been demonstrated by imaging with a simple doublet lens.

High-resolution confocal microscopy with low-NA objectives based on diffractive lens arrays.

High resolution and large fields of view are difficult to achieve simultaneously by microscope objectives. In this work, we develop a reflection confocal microscope based on diffractive lens arrays to solve the problem. We demonstrate a prototype that generates a spot array with a numerical aperture of 0.78. Laterally, experiments show a spatial cutoff frequency of 1024 lp/mm by a 0.15 NA objective, and 912 lp/mm by a 0.07 NA objective with a 785 nm diode laser. Axially, an average height of 961 nm with a standard deviation of 49 nm is measured with a 925.5 nm calibrated step height target.

Analytical determination of the complex refractive index and the incident angle of an optically isotropic substrate by ellipsometric parameters and reflectance.

An analytical solution for the determination of both angle of incidence (AOI) and the complex refractive index from combined ellipsometric and reflectometric measurements at optically isotropic substrates is presented. Conventional ellipsometers usually measure flat surfaces because the curvatures of the surface alter the reflected or transmitted light, which causes experimental errors due to the deviation of the incident angle. However, in real industrial applications, the shapes of samples are usually curved or even free-form. In this case, the knowledge of the AOI is essential. The proposed method provides a simple way to measure the AOI and the complex refractive index of nonplanar samples without extra or complicated hardware.

An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection.

Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications.

SmartSpectrometer-Embedded Optical Spectroscopy for Applications in Agriculture and Industry.

The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder.

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Programmable light source based on an echellogram of a supercontinuum laser.

Illumination systems with tunable spectrums have been receiving an increasing amount of attention due to their wide application and unique capability. This paper proposes a programmable light source in the visible range based on the combination of a prism and an echelle grating. A supercontinuum laser is utilized as the primary source, whose echellogram is projected to a digital mirror device for wavelength selection. A complete calibration procedure is developed to generate any target spectrum of choice. Experiments have shown that spectral peaks with a full width at half-maximum of 1 nm can be easily generated and the wavelength tuning resolution can reach as small as 0.01 nm.