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Cognitive Weeding: An Approach to Single-plant Specific Weed Regulation

KUNSTLICHE INTELLIGENZ 2023年第2-4期37卷 175-181页

作者： Niemeyer, Mark Renz, Marian Pukrop, Maren Hagemann, David Zurheide, Tim Di Marco, Daniel Hoeferlin, Markus Stark, Philipp Rahe, Florian Igelbrink, Matthias Jenz, Mario Jarmer, Thomas Trautz, Dieter Stiene, Stefan Hertzberg, Joachim German Res Ctr Artificial Intelligence Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Osnabruck Germany Univ Appl Sci Osnabruck Osnabruck Germany Farming Revolut Ludwigsburg Germany Amazonen Werke Hasbergen Germany

This paper provides a comprehensive overview of the architecture required to implement selective weeding in arable farming, as developed within the Cognitive Weeding project. This end-to-end architecture begins with data acquisition utilizing drones, robots, or agricultural machinery, followed by data management, AI-based data annotation, knowledge-based inference to determine the necessary treatment, resulting in an application map for selective hoeing. The paper meticulously details the various components of the architecture and illustrates through examples how they are interconnected.

关键词： selective weeding site-specific weed management precision agriculture expert system

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COMPUTING ARBITRARILY LARGE MESHES WITH LEVEL-OF-DETAIL SUPPORT FOR CESIUM 3D TILES 7

COMPUTING ARBITRARILY LARGE MESHES WITH LEVEL-OF-DETAIL SUPP...

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7th International Workshop on LowCost 3D - Sensors, Algorithms, Applications

作者： Hillmann, Malte Igelbrink, Felix Wiemann, Thomas Osnabruck Univ Knowledge Based Syst Grp D-49080 Osnabruck Germany DFKI Niedersachsen Plan Based Robot Control D-49080 Osnabruck Germany

In this paper we present an approach to compute arbitrarily sized meshes of large scale environments. The meshes can be reconstructed from laser-scanned point clouds or existing high-resolution meshes. The algorithm automatically builds a level of detail hierarchy in the Cesium 3D Tiles format using an octree partition. The main contribution of this paper is a method that ensures that the generated meshes for each level-of-detail stage are computed in a consistent manner to minimize visual artifacts between different detail levels during rendering. Furthermore, both the reconstruction and simplification algorithm are designed to constrain the memory consumption, which enables to process even very large data sets on consumer-grade hardware. The export into the Cesium 3D Tiles format allows to render such large meshes efficiently in all web-based viewers that support this format. In our experiments we evaluate the method on different datasets and assess the visual quality during the rendering process and analyze the memory footprint as well as the runtime behaviour.

关键词： 3D Surface Reconstruction Mesh Simplification Level-of-Detail Rendering Web-based Visualization

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Hyperspectral 3D Point Cloud Segmentation Using RandLA-Net 17th

Hyperspectral 3D Point Cloud Segmentation Using RandLA-Net

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17th International Conference on Intelligent Autonomous Systems (IAS)

作者： Mitschke, Isaak Wiemann, Thomas Igelbrink, Felix Hertzberg, Joachim Osnabruck Univ Inst Comp Sci Berghoffstr 11 D-49090 Osnabruck Germany DFKI Niedersachsen Lab Plan Based Robot Control Berghoffstr 11 D-49090 Osnabruck Germany

ISBN: (纸本)9783031222153;9783031222160

Point clouds are commonly used in robotics to represent 3D maps. To gain further understanding of their content, it is useful to annotate such maps semantically. To segment 3D point clouds with RGB values, different solutions exist. In machine learning, pre-trained classifiers are used for this purpose. Since it is not always possible to differentiate between entities relying solely on RGB information, hyperspectral histograms can augment the 3D data. The aim of this work is to evaluate, if hyperspectral information can improve the segmentation results for ambiguous objects, e.g., streets, sidewalks, and cars using established deep learning methods. Given the reported performance on geometrical 3D data and the possibility to directly integrate point annotations, we extended the neural network RandLA-Net. In addition to the evaluation of RandLA-Net in this context, we also provide a reference dataset consisting of semantically annotated hyperspectral 3D point clouds.

关键词： Point clouds Semantic segmentation Deep learning Hyperspectral imaging

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AI-based Maize and Weeds Detection on the Edge with CornWeed Dataset 18

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023

作者： Iqbal, Naeem Manss, Christoph Scholz, Christian Konig, Daniel Igelbrink, Matthias Ruckelshausen, Arno Dfki Plan-based Robot Control Osnabrueck Germany Dfki Marine Perception Oldenburg Germany University of Applied Sciences Osnabrueck Faculty of Engineering and Computer Science Osnabrueck Germany

ISBN: (纸本)9788396744784

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange. © 2023 Polish Information Processing Society.

关键词： Data acquisition

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Segmentation of Multibeam Echosounder Bathymetry and Backscatter

Segmentation of Multibeam Echosounder Bathymetry and Backsca...

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OCEANS Conference

作者： Coffelt, Jeremy Paul Smith, Amos Conen, Niklas Kampmann, Peter Univ Bremen Inst AI Bremen Germany German Res Ctr AI DFKI Plan Based Robot Control Osnabruck Germany ROSEN Creat Ctr Subsea Solut Lingen Germany ROSEN Creat Ctr Subsea Solut Bremen Germany

ISBN: (纸本)9798350362077

Multibeam echosounders (MBES) are the tool of choice for high-precision underwater surveys, especially when water conditions render optical imagery ineffective. We present and evaluate the following approaches for MBES segmentation: (1) real-time processing of single sounding profiles using traditional machine learning techniques, (2) batch processing of "waterfall" pseudo-images using a standard U-Net model, (3) the same model adapted to 2D projections of 3D point clouds, and (4) post-mission, survey-level processing using modern networks specifically designed for sparse point clouds. Strengths and weaknesses of the methods are discussed, including data preprocessing requirements, robustness, and ease of implementation/interpretation. Evaluation is performed on real data collected by an autonomous underwater vehicle (AUV) during a deep-sea industrial pipeline inspection.

关键词： underwater perception image and cloud segmentation multibeam sonar marine robots pipeline inspection

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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ray Tracing for Embedded Hardware on Mobile robots

Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ra...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mock, Alexander Wiemann, Thomas Hertzberg, Joachim Osnabruck Univ Inst Comp Sci Knowledge Based Syst Grp Berghoffstr 11 D-49090 Osnabruck Germany Osnabruck Univ Inst Comp Sci Amonomous Robot Grp Berghoffstr 11 D-49090 Osnabruck Germany DFKI Niedersachsen Plan Based Robot Control Berghoffstr 11 D-49090 Osnabruck Germany

ISBN: (纸本)9798350323658

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking. However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library, called Rmagine, allows a robot to simulate sensor data for arbitrary range sensors directly on board via ray tracing. Since robots typically only have limited computational resources, Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like NVIDIA Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems.

关键词： Ray tracing

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A Deep Learning Framework for Semantic Segmentation of Underwater Environments

A Deep Learning Framework for Semantic Segmentation of Under...

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OCEANS Hampton Roads Conference

作者： Smith, Amos Coffelt, Jeremy Lingemann, Kai German Res Ctr Artificial Intelligence DFKI Plan Based Robot Control Osnabruck Germany Univ Bremen Inst Artificial Intelligence Bremen Germany

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Perception tasks such as object classification and segmentation are crucial to the operation of underwater robotics missions like bathymetric surveys and infrastructure inspections. Marine robots in these applications typically use a combination of laser scanner, camera, and sonar sensors to generate images and point clouds of the environment. Traditional perception approaches often struggle to overcome water turbidity, light attenuation, marine snow, and other harsh conditions of the underwater world. Deep learning-based perception techniques have proven capable of overcoming such difficulties, but are often limited by the availability of relevant training data. In this paper, we propose a framework that consists of procedural creation of randomized underwater pipeline environment scenes, the generation of corresponding point clouds with semantic labels, and the training of a 3D segmentation network using the synthetic data. The resulting segmentation network is analyzed on real underwater point cloud data and compared with a traditional baseline approach.

关键词： underwater robotics 3D point cloud segmentation deep learning perception data simulation

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Towards model-based automation of plant-specific weed regulation 43

Towards model-based automation of plant-specific weed regula...

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43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023

作者： Renz, Marian Niemeyer, Mark Hertzberg, Joachim German Research Center for Artificial Intelligence Plan-Based Robot Control Group Berghoffstr. 11 Osnabrück49090 Germany Osnabrück University Knowledge-Based Systems Group Wachsbleiche 27 Osnabrück49090 Germany

ISBN: (纸本)9783885797241

Weeds are commonly known as a major factor for yield losses in agriculture, competing with crops for resources like nutrients, water, and light. However, keeping specific weeds could benefit agricultural sites for example by nitrogen fixation, erosion protection, or increasing biodiversity. This comes with technological challenges like plant detection and classification, damage estimation, and selective removal. This paper presents a model-based approach to the problem of damage estimation of perceived plants. The system uses contextual and background knowledge in the form of rules about the plant count per square meter and the distance to the nearest crop together with thresholds for each weed species. The functionality is demonstrated using an artificial dataset and exemplary thresholds, showing the potential of using knowledge about plant-crop interactions for more sophisticated weed control systems. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Crops

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A Spatio-Temporal-Semantic Environment Representation for Autonomous Mobile robots equipped with various Sensor Systems

A Spatio-Temporal-Semantic Environment Representation for Au...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Niemeyer, Mark Puetz, Sebastian Hertzberg, Joachim Plan Based Robot Control Grp German Res Ctr Artificial Intelligence Osnabruck Germany Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany Nat Robots GmbH Osnabruck Germany

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

The large amount of high resolution sensor data, both temporal and spatial, that autonomous mobile robots collect in today's systems requires structured and efficient management and storage during the robot mission. In response, we present SEEREP: A Spatio-Temporal-Semantic Environment Representation for Autonomous Mobile robots. SEEREP handles various types of data at once and provides an efficient query interface for all three modalities that can be combined for high-level analyses. It supports common robotic sensor data types such as images and point clouds, as well as sensor and robot coordinate frames changing over time. Furthermore, SEEREP provides an efficient HDF5-based storage system running on the robot during operation, compatible with ROS and the corresponding sensor message definitions. The compressed HDF5 data backend can be transferred efficiently to an application server with a running SEEREP query server providing gRPC interfaces with Protobuf and Flattbuffer message types. The query server can support high-level planning and reasoning systems in e.g. agricultural environments, or other partially unstructured environments that change over time. In this paper we show that SEEREP is much better suited for these tasks than a traditional GIS, which cannot handle the different types of robotic sensor data.

关键词： Three-dimensional displays Annotations robot kinematics Semantics robot sensing systems Cognition planning

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Leveraging Explainable AI Methods Towards Identifying Classification Issues on IDS Datasets 48

Leveraging Explainable AI Methods Towards Identifying Classi...

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48th Conference on Local Computer Networks

作者： Lanfer, Eric Sylvester, Sophia Aschenbruck, Nils Atzmueller, Martin Osnabruck Univ Inst Comp Sci Osnabruck Germany Norwegian Univ Sci & Technol NTNU Dept Mental Hlth Trondheim Norway German Res Ctr AI DFKI Plan Based Robot Control Osnabruck Germany

ISBN: (纸本)9798350300741;9798350300734

Nowadays, anomaly-based network intrusion detection system (NIDS) still have limited real-world applications;this is particularly due to false alarms, a lack of datasets, and a lack of confidence. In this paper, we propose to use explainable artificial intelligence (XAI) methods for tackling these issues. In our experimentation, we train a random forest (RF) model on the NSL-KDD dataset, and use SHAP to generate global explanations. We find that these explanations deviate substantially from domain expertise. To shed light on the potential causes, we analyze the structural composition of the attack classes. There, we observe severe imbalances in the number of records per attack type subsumed in the attack classes of the NSL-KDD dataset, which could lead to generalization and overfitting regarding classification. Hence, we train a new RF classifier and SHAP explainer directly on the attack types. Classification performance is considerably improved, and the new explanations are matching the expectations based on domain knowledge better. Thus, we conclude that the imbalances in the dataset bias classification and consequently also the results of XAI methods like SHAP. However, the XAI methods can also be employed to find and debug issues and biases in the data and the applied model. Furthermore, the debugging results in higher trustworthiness of anomaly-based NIDS.

关键词： Explainable AI Cybersecurity Network Intrusion Detection NSL-KDD Feature Importance SHAP

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