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Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.
Spatial precision and recall indices to assess the performance of instance segmentation algorithms
(2022)
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.
Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.
Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test.
The decarbonization of for example the energy or heat sector leads to the transformation of distribution grids. The expansion of decentralized energy resources and the integration of new consumers due to sector coupling (e.g. heat pumps or electric vehicles) into low voltage grids increases the need for grid expansion and usage of flexibilities in the grid. A high observability of the current grid status is needed to perform these tasks efficiently and effectively. Therefore, there is a need to increase the observability of low voltage grids by installing measurement technologies (e.g. smart meters). Multiple different measurement technologies are available for low voltage grids which can vary in their benefit to observation quality and their installation costs. Therefore, Bayernwerk Netz GmbH and E.DIS AG in cooperation with E-Bridge Consulting GmbH and the Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW) investigated the effectiveness of different strategies for the smartification of low voltage grids. This paper presents the methodology used for the investigation and exemplary results focusing on the impact of intelligent cable distribution cabinets and smart meters on the quality of the state estimation.
Die Zweite Neubekanntmachung der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 02.05.2023 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 882) ist wie folgt zu berichtigen:
In § 20 Abs. 1 S. 1 werden die Wörter „dokumentenecht gebundener Ausfertigung und“ gestrichen.
Nachstehend wird der Wortlaut der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 29.08.2016 in der Fassung der Neubekanntmachung vom 14.12.2018 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 641) neu bekannt gemacht. Die Zweite Neubekanntmachung berücksichtigt die Zweite Satzung zur Änderung der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 19.12.2019 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 687) sowie die Dritte Satzung zur Änderung der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 10.03.2021 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 762).
Nachstehend wird der Wortlaut der Prüfungsordnung für den Bachelorstudiengang Wirtschaftsingenieurwesen Elektrotechnik an der Hochschule Düsseldorf vom 13.09.2017 in der Fassung der Neubekanntmachung vom 14.12.2018 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 641) neu bekannt gemacht. Die Zweite Neubekanntmachung berücksichtigt die Zweite Satzung zur Änderung der Prüfungsordnung für den Bachelorstudiengang Wirtschaftsingenieurwesen Elektrotechnik an der Hochschule Düsseldorf vom 19.12.2019 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 686), die Dritte Satzung zur Änderung der Prüfungsordnung für den Bachelorstudiengang Wirtschaftsingenieurwesen Elektrotechnik an der Hochschule Düsseldorf vom 28.02.2020 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 692) sowie die Vierte Satzung zur Änderung der Prüfungsordnung für den Bachelorstudiengang Wirtschaftsingenieurwesen Elektrotechnik an der Hochschule Düsseldorf vom 10.03.2021 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 761).
Nachstehend wird der Wortlaut der Prüfungsordnung für die Bachelorstudiengänge Elektro- und Informationstechnik und Elektro- und Informationstechnik (dual) an der Hochschule Düsseldorf vom 29.08.2016 in der Fassung der Neubekanntmachung vom 14.12.2018 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 640) neu bekannt gemacht. Die Zweite Neubekanntmachung berücksichtigt die Zweite Satzung zur Änderung der Prüfungsordnung für die Bachelorstudiengänge Elektro- und Informationstechnik und Elektro- und Informationstechnik (dual) an der Hochschule Düsseldorf vom 13.02.2019 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 629), die Dritte Satzung zur Änderung der Prüfungsordnung für die Bachelorstudiengänge Elektro- und Informationstechnik und Elektro- und Informationstechnik (dual) an der Hochschule Düsseldorf vom 19.12.2019 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 680), die Vierte Satzung zur Änderung der Prüfungsordnung für die Bachelorstudiengänge Elektro- und Informationstechnik und Elektro- und Informationstechnik (dual) an der Hochschule Düsseldorf vom 28.02.2020 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 691) sowie die Fünfte Satzung zur Änderung der Prüfungsordnung für die Bachelorstudiengänge Elektro- und Informationstechnik und Elektro- und Informationstechnik (dual) an der Hochschule Düsseldorf vom 10.03.2021 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 759).