Beleuchtungsverfahren zur problemspezifischen Bildgewinnung für die automatische Sichtprüfung

Der Beleuchtungsentwurf in der automatischen Sichtprüfung ist von zentraler Bedeutung und hat enormen Einfluss auf die Leistungsfähigkeit eines Sichtprüfsystems. In dieser Arbeit wird ein neuartiger problemspezifischer Beleuchtungsentwurf vorgestellt, der durch eine optische, in die physikalische Bildgewinnung vorgelagerte, Merkmalsextraktion motiviert ist. Der Ansatz wird auf Grundlage eines physikalisch begründeten Kamera- und Beleuchtungsmodells signaltheoretisch analysiert sowie im Rahmen verschiedener Anwendungsszenarien experimentell evaluiert

Beleuchtungsverfahren zur problemspezifischen Bildgewinnung für die automatische Sichtprüfung

In machine vision, the illumination design has direct impact on the performance of a visual inspection system. In this work, a problem-specific illumination design is presented, which is motivated by optical feature extraction taking place during physical image acquisition. The approach is analyzed on the basis of a physically based camera and illumination model and is experimentally evaluated in different application scenarios

Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis

Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble

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

TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials

Optical belt sorters are a versatile, state-of-the-art technology to sort bulk materials that are hard to sort based on only nonvisual properties. In this paper, we propose an extension to current optical belt sorters that involves replacing the line camera with an area camera to observe a wider field of view, allowing us to observe each particle over multiple time steps. By performing multitarget tracking, we are able to improve the prediction of each particle‘s movement and thus enhance the performance of the utilized separation mechanism. We show that our approach will allow belt sorters to handle new classes of bulk materials while improving cost efficiency. Furthermore, we lay out additional extensions that are made possible by our new paradig

Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison

A DEM-CFD (discrete element method - computational fluid dynamics) model of an optical belt sorter was extensively compared with experiments of a laboratory-scale sorter to assess the model\u27s accuracy. Brick and sand-lime brick were considered as materials. First, the transport characteristics on the conveyor belt, involving mass flow, lateral particle distribution and proximity, were compared. Second, sorting results were benchmarked for varying mixture proportions at differing mass flows. It was found that the numerical model is able to reproduce the experimental results with high accuracy

Increasing the reuse of wood in bulky waste using artificial intelligence and imaging in the VIS, IR, and terahertz ranges

Bulky waste contains valuable raw materials, especially wood, which accounts for around 50% of the volume. Sorting is very time-consuming in view of the volume and variety of bulky waste and is often still done manually. Therefore, only about half of the available wood is used as a material, while the rest is burned with unsorted waste. In order to improve the material recycling of wood from bulky waste, the project ASKIVIT aims to develop a solution for the automated sorting of bulky waste. For that, a multi-sensor approach is proposed including: (i) Conventional imaging in the visible spectral range; (ii) Near-infrared hyperspectral imaging; (iii) Active heat flow thermography; (iv) Terahertz imaging. This paper presents a demonstrator used to obtain images with the aforementioned sensors. Differences between the imaging systems are discussed and promising results on common problems like painted materials or black plastic are presented. Besides that, pre-examinations show the importance of near-infrared hyperspectral imaging for the characterization of bulky waste