Search

Der Zylinderkopf und das Kurbelgehäuse gehören zu den meist beanspruchten Gussbauteilen im Verbrennungsmotor. Die hohe Funktionsintegration und der gleichzeitige Wandel der Automobilindustrie zum Leichtbau führen zu geometrisch hochkomplexen sowie mechanisch und thermisch stark belasteten Gussbauteilen. Die Inline-Computertomographie (CT) wird eingesetzt, um die geforderte Auslieferqualität jedes Bauteils im Takt der Fertigungslinie zu garantieren. Die Herausforderung bei der Prüfung besteht in der geringen zur Verfügung stehenden Prüfzeit. Der Takt der Fertigungsstraße, welcher teilweise weniger als eine Minute beträgt, gibt den Zeitrahmen für die Prüfung vor. Eine weitere zu bewältigende Aufgabe entsteht durch den Herstellungsprozess der Bauteile. Der eingesetzte Kokillenguss führt zu individuellen Abweichungen jedes Bauteiles von der Idealgeometrie, was den einfachen Vergleich zur Sollgeometrie unmöglich macht. Durch den Einsatz der CT-Prüfung können diese Abweichungen aufgezeigt, analysiert und bewertet werden. Die umfangreiche Analyse der Zylinderköpfe und Kurbelgehäuse in Kombination mit einem fehlenden Standard für die CT-spezifische Defektauswertung führen zu einem enorm hohen manuellen Aufwand. Der Einfluss des jeweiligen Mitarbeiters prägt dabei ein stark subjektiv beeinflusstes Prüfergebnis für jedes Bauteil. Die im Folgenden beschriebenen Weiterentwicklungen beziehen sich auf die automatische Analyse und Bewertung durch ein Inline-CT für Zylinderköpfe. Im Ergebnis kann eine effiziente Qualitätssicherung bei gleichzeitiger Reduktion des menschlichen Einflusses bei der Bauteilbewertung erreicht werden. One of the most stressed cast components of an internal combustion engine are the cylinder head and cylinder block. A large number of functionalities needs to be integrated into the product next to complying to lightweight construction demands. Hence, the cylinder head is a very complex and mechanically stressed cast component. Inline computed tomography ( CT \mathit{CT}) is used to guarantee the desired standards in each produced part within the fast cycle time of the production line. The main challenge for the inline CT is the short time being available for the quality inspection. The cycle time of the production line can be less than one minute and is therefore the upper limit of the quality inspection time for each part. Furthermore, due to the gravity die casting process natural deviations from the ideal target geometry occur. The deviations vary over time and are, thus, individual for each part and prevent a simple comparison to the nominal geometry. Those deviations can be evaluated by inline CT. The extensive analysis of each cylinder head in combination with a missing standard for the inline CT-based defect evaluation lead to high manual demands. The impact of the respective co-worker implies a strongly subjectively influenced part quality. In the following the recent developments regarding the analysis and evaluation using inline CT technology for cylinder heads are described. An efficient quality assurance and reduction of the manual influence on the evaluation is the result. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a software tool for automated detection of focal hyperpigmentary changes (FHC) in eyes with intermediate age-related macular degeneration (AMD)., Methods: Color fundus (CFP) and autofluorescence (AF) photographs of 33 eyes with FHC of 28 AMD patients (mean age 71 years) from the prospective longitudinal natural history MODIAMD-study were included. Fully automated to semiautomated registration of baseline to corresponding follow-up images was evaluated. Following the manual circumscription of individual FHC (four different readings by two readers), a machine-learning algorithm was evaluated for automatic FHC detection., Results: The overall pixel distance error for the semiautomated (CFP follow-up to CFP baseline: median 5.7; CFP to AF images from the same visit: median 6.5) was larger as compared for the automated image registration (4.5 and 5.7; P < 0.001 and P < 0.001). The total number of manually circumscribed objects and the corresponding total size varied between 637 to 1163 and 520,848 pixels to 924,860 pixels, respectively. Performance of the learning algorithms showed a sensitivity of 96% at a specificity level of 98% using information from both CFP and AF images and defining small areas of FHC ("speckle appearance") as "neutral.", Conclusions: FHC as a high-risk feature for progression of AMD to late stages can be automatically assessed at different time points with similar sensitivity and specificity as compared to manual outlining. Upon further development of the research prototype, this approach may be useful both in natural history and interventional large-scale studies for a more refined classification and risk assessment of eyes with intermediate AMD., Translational Relevance: Automated FHC detection opens the door for a more refined and detailed classification and risk assessment of eyes with intermediate AMD in both natural history and future interventional studies.