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PublicationRoot cause analysis using anomaly detection and temporal informed causal graphs( 2024)In industrial processes, anomalies in the production equipment may lead to expensive failures. To avoid and avert such failures, the identification of the right root cause is crucial. Ideally, the search for a root cause is backed by causal information such as causal graphs. We have extended a framework that fuses causal graphs with anomaly detection to infer likely root causes. In this work, we add the use of temporal information to draw temporal valid conclusions about the potential propagation of anomalous information in causal graphs. The use of the framework is demonstrated on a robotic gripping process.
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PublicationSecurity Fence Inspection at Airports Using Object Detection( 2024)To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this pur-pose, security fences are commonly used, but they require regular inspection to detect damages. However, due to the growing shortage of human specialists and the large man-ual effort, there is the need for automated methods. The aim is to automatically inspect the fence for damage with the help of an autonomous robot. In this work, we explore object detection methods to address the fence inspection task and localize various types of damages. In addition to evaluating four State-of-the-Art (SOTA) object detection models, we analyze the impact of several design criteria, aiming at adapting to the task-specific challenges. This in-cludes contrast adjustment, optimization of hyperparameters, and utilization of modern backbones. The experimental results indicate that our optimized You Only Look Once v5 (YOLOv5) model achieves the highest accuracy of the four methods with an increase of 6.9% points in Average Precision (AP) compared to the baseline. Moreover, we show the real-time capability of the model. The trained models are published on GitHub: hups://github.com/IN-Friederichlairport_fence_inspection.
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PublicationUnderstanding, describing, and mitigating the flow of personal data in ROS 2 systems to comply with the GDPR and beyond.*( 2024)With the introduction of privacy protection laws such as the GDPR in the EU, carrying out privacy impact assessments (PIA) for new robotics applications interacting with humans is becoming a necessary part of the development process. We discuss a methodology and develop and release software [1] to describe and mitigate the flow of personal data as part of the development process or retroactively in the Robot Operating System - ROS 2.
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PublicationSynthAct: Towards Generalizable Human Action Recognition based on Synthetic Data( 2024)Synthetic data generation is a proven method for augmenting training sets without the need for extensive setups, yet its application in human activity recognition is underexplored. This is particularly crucial for human-robot collaboration in household settings, where data collection is often privacy-sensitive. In this paper, we introduce SynthAct, a synthetic data generation pipeline designed to significantly minimize the reliance on real-world data. Leveraging modern 3D pose estimation techniques, SynthAct can be applied to arbitrary 2D or 3D video action recordings, making it applicable for uncontrolled in-the-field recordings by robotic agents or smarthome monitoring systems. We present two SynthAct datasets: AMARV, a large synthetic collection with over 800k multi-view action clips, and Synthetic Smarthome, mirroring the Toyota Smarthome dataset. SynthAct generates a rich set of data, including RGB videos and depth maps from four synchronized views, 3D body poses, normal maps, segmentation masks and bounding boxes. We validate the efficacy of our datasets through extensive synthetic-to-real experiments on NTU RGB+D and Toyota Smarthome. SynthAct is available on our project page 4 .
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PublicationMetrics for the evaluation of learned causal graphs based on ground truth( 2024)The self-guided learning of causal relations may contribute to the general maturity of artificial intelligence in the future. To develop such learning algorithms, powerful metrics are required to track advances. In contrast to learning algorithms, little has been done in regards to developing suitable metrics. In this work, we evaluate current state of the art metrics by inspecting their discovery properties and their considered graphs. We also introduce a new combination of graph notation and metric, which allows for benchmarking given a variety of learned causal graphs. It also allows the use of maximal ancestral graphs as ground truth.
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PublicationUtilizing multispectral imaging for improved weed and crop detection( 2024)
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PublicationFew-Shot Semantic Segmentation for Complex Driving Scenes( 2024)The main objective of few-shot semantic segmentation (FSSS) is to segment novel objects within query images by leveraging a limited set of support images. Being capable of segmenting the novel classes plays an essential role in the development of perception functions for automated vehicles. However, existing few-shot semantic segmentation work strives to improve the performance of the models on object-centric datasets. In our work, we evaluate the few-shot semantic segmentation on the more challenging driving scene understanding tasks. As a use case specific study, we give a systematic analysis of the disparity between commonly used FSSS datasets and driving datasets. Based on that, we proposed methodologies to integrate knowledge from the class hierarchy of the datasets, utilize more effective feature extraction, and choose more representative support images during inference. These approaches are evaluated extensively on the Cityscapes and Mapillary datasets to indicate their effectiveness. We point out the remaining challenges of training, evaluating, and employing FSSS models for complex road scenes in real practice.
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PublicationAutomatische Sichtprüfung. 3. Aufl.(Springer Vieweg, 2024)Das Lehrbuch behandelt systematisch die Bildgewinnung für die automatische Sichtprüfung. Die Autoren leiten die wesentlichen Methoden detailliert ab und stellen alle gängigen Bildgewinnungsverfahren in einem strukturierten Zusammenhang dar. Der zweite Teil des Buches ist der Bildsignalbeschreibung und der Bildauswertung gewidmet, wobei insbesondere Methoden behandelt werden, die für die automatische Sichtprüfung relevant sind. Die Autoren skizzieren die Herleitung der beschriebenen Methoden, ohne sich in mathematischen Details zu verlieren. Ihr Ziel ist, dass der Leser die Zusammenhänge wirklich versteht und das "große Bild" des Fachgebietes erkennt. Das Buch ist in sich geschlossen und bedarf zum Verständnis keiner ergänzenden Literatur. Die 3. Auflage wurde an vielen Stellen verbessert und unter anderem durch die detaillierte Einführung von neuronalen Faltungsnetzen auf den aktuellen Stand der Technik aktualisiert.
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PublicationKnowledge-Distillation-Based Label Smoothing for Fine-Grained Open-Set Vehicle Recognition( 2024)Fine-grained vehicle classification describes the task of estimating the make and the model of a vehicle based on an image. It provides a useful tool for security authorities to find suspects in surveillance cameras. However, most research about fine-grained vehicle classification is only focused on a closed-set scenario which considers all possible classes to be included in the training. This is not realistic for real-world surveillance applications where the images fed into the classifier can be of arbitrary vehicle models and the large number of commercially available vehicle models renders learning all models impossible. Thus, we investigate fine-grained vehicle classification in an open-set recognition scenario which includes unknown vehicle models in the test set and expects these samples to be rejected. Our experiments highlight the importance of label smoothing for open-set recognition performance. Nonetheless, it lacks recognizing the different semantic distances between vehicle models which result in largely different confusion probabilities. Thus, we propose a knowledge-distillation-based label smoothing approach which considers these different semantic similarities and thus, improves the closed-set classification as well as the open-set recognition performance.
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Publication6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly( 2024)The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of product life cycles. A pivotal objective in this context involves the identification and 6D pose estimation of bolts affixed to the motors, facilitating automated disassembly within the manufacturing workflow. Complicating matters, the presence of occlusions and the limitations of single-view data acquisition, notably when motors are placed in a clamping system, obscure certain portions and render some bolts imperceptible. Consequently, the development of a comprehensive pipeline capable of acquiring complete bolt information is imperative to avoid oversight in bolt detection. In this paper, employing the task of bolt detection within the scope of our project as a pertinent use case, we introduce a meticulously devised pipeline. This multi-stage pipeline effectively captures the 6D information with regard to all bolts on the motor, thereby showcasing the effective utilization of prior knowledge in handling this challenging task. The proposed methodology not only contributes to the field of 6D pose estimation but also underscores the viability of integrating domain-specific insights to tackle complex problems in manufacturing and automation.
