6 research outputs found
Determining Cost-Efficient Controls of Electrical Energy Storages Using Dynamic Programming
Volatile electrical energy prices are a challenge and an opportunity for
small and medium-size companies in energy-intensive industries. By using
electrical energy storage and/or an adaptation of production processes,
companies can significantly profit from time-depending energy prices and reduce
their energy costs.
We consider a time-discrete optimal control problem to reach a desired final
state of the energy storage at a certain time step. Thereby, the energy input
is discrete since only multiples of 100 kWh can be purchased at the EPEX SPOT
market. We use available price estimations to minimize the total energy cost by
a rounding based dynamic programming approach. With our model non-linear energy
loss functions of the storage can be considered and we obtain a significant
speed-up compared to the integer (linear) programming formulation
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Deep neural networks (DNN) which are employed in perception systems for
autonomous driving require a huge amount of data to train on, as they must
reliably achieve high performance in all kinds of situations. However, these
DNN are usually restricted to a closed set of semantic classes available in
their training data, and are therefore unreliable when confronted with
previously unseen instances. Thus, multiple perception datasets have been
created for the evaluation of anomaly detection methods, which can be
categorized into three groups: real anomalies in real-world, synthetic
anomalies augmented into real-world and completely synthetic scenes. This
survey provides a structured and, to the best of our knowledge, complete
overview and comparison of perception datasets for anomaly detection in
autonomous driving. Each chapter provides information about tasks and ground
truth, context information, and licenses. Additionally, we discuss current
weaknesses and gaps in existing datasets to underline the importance of
developing further data.Comment: Accepted for publication at IV 202
Detecting Novelties with Empty Classes
For open world applications, deep neural networks (DNNs) need to be aware of
previously unseen data and adaptable to evolving environments. Furthermore, it
is desirable to detect and learn novel classes which are not included in the
DNNs underlying set of semantic classes in an unsupervised fashion. The method
proposed in this article builds upon anomaly detection to retrieve
out-of-distribution (OoD) data as candidates for new classes. We thereafter
extend the DNN by empty classes and fine-tune it on the OoD data samples.
To this end, we introduce two loss functions, which 1) entice the DNN to assign
OoD samples to the empty classes and 2) to minimize the inner-class feature
distances between them. Thus, instead of ground truth which contains labels for
the different novel classes, the DNN obtains a single OoD label together with a
distance matrix, which is computed in advance. We perform several experiments
for image classification and semantic segmentation, which demonstrate that a
DNN can extend its own semantic space by multiple classes without having access
to ground truth.Comment: 13 pages, 13 figures, 4 table
Detecting and Learning the Unknown in Semantic Segmentation
Chan RK-W, Uhlemeyer S, Rottmann M, Gottschalk H. Detecting and Learning the Unknown in Semantic Segmentation. In: Fingscheidt T, Gottschalk H, Houben S, eds. Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety. Cham: Springer International Publishing; 2022: 277-313.Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task, and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known asanomalies, which are extremely safety-critical to properly cope with. In this chapter, we first give an overview about anomalies from an information-theoretic perspective. Next, we review research in detecting unknown objects in semantic segmentation. We present a method outperforming recent approaches by training for high entropy responses on anomalous objects, which is in line with our theoretical findings. Finally, we propose a method to assess the occurrence frequency of anomalies in order to select anomaly types to include into a model’s set of semantic categories. We demonstrate that those anomalies can then be learned in an unsupervised fashion which is particularly suitable in online applications
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Chan RK-W, Lis K, Uhlemeyer S, et al. SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. arXiv:2104.14812. 2021.State-of-the-art semantic or instance segmentation deep neural networks
(DNNs) are usually trained on a closed set of semantic classes. As such, they
are ill-equipped to handle previously-unseen objects. However, detecting and
localizing such objects is crucial for safety-critical applications such as
perception for automated driving, especially if they appear on the road ahead.
While some methods have tackled the tasks of anomalous or out-of-distribution
object segmentation, progress remains slow, in large part due to the lack of
solid benchmarks; existing datasets either consist of synthetic data, or suffer
from label inconsistencies. In this paper, we bridge this gap by introducing
the "SegmentMeIfYouCan" benchmark. Our benchmark addresses two tasks: Anomalous
object segmentation, which considers any previously-unseen object category; and
road obstacle segmentation, which focuses on any object on the road, may it be
known or unknown. We provide two corresponding datasets together with a test
suite performing an in-depth method analysis, considering both established
pixel-wise performance metrics and recent component-wise ones, which are
insensitive to object sizes. We empirically evaluate multiple state-of-the-art
baseline methods, including several models specifically designed for anomaly /
obstacle segmentation, on our datasets and on public ones, using our test
suite. The anomaly and obstacle segmentation results show that our datasets
contribute to the diversity and difficulty of both data landscapes
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
Maag K, Chan RK-W, Uhlemeyer S, Kowol K, Gottschalk H. Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. In: Wang L, Gall J, Chin T-J, Sato I, Chellappa R, eds. Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V. Lecture Notes in Computer Science. Vol 13845. Cham: Springer Nature Switzerland; 2023: 476-494.In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying image segmentation algorithm, or an instance within the semantic space which however looks decisively different from the instances contained in the training data. OOD objects occurring on video sequences should be detected on single frames as early as possible and tracked over their time of appearance as long as possible. During the time of appearance, they should be segmented as precisely as possible. We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects. We furthermore publish the synthetic CARLA-WildLife data set that consists of 26 video sequences containing up to four OOD objects on a single frame. We propose metrics to measure the success of OOD tracking and develop a baseline algorithm that efficiently tracks the OOD objects. As an application that benefits from OOD tracking, we retrieve OOD sequences from unlabeled videos of street scenes containing OOD objects