Alt-Text: Teilnehmer des Kick-Off-Treffens des ReVise-UP Forschungsvorhabens stehen vor dem Bergbaugebäude der RWTH Aachen University. // Participants of the kick-off meeting of the ReVise-UP research project stand in front of the mining building of RWTH Aachen University.

Deutsche Version

Forschungsvorhaben „ReVise-UP“ zur Verbesserung der Prozesseffizienz des werkstofflichen Kunststoffrecyclings mittels Sensortechnik gestartet

Im September 2023 startete das vom BMBF geförderte Forschungsvorhaben ReVise-UP („Verbesserung der Prozesseffizienz des werkstofflichen Recyclings von Post-Consumer Kunststoff-Verpackungsabfällen durch intelligentes Stoffstrommanagement – Umsetzungsphase“).  In der vierjährigen Umsetzungsphase soll die Transparenz und Effizienz des werkstofflichen Kunststoffrecyclings durch Entwicklung und Demonstration sensorbasierter Stoffstromcharakterisierungsmethoden im großtechnischen Maßstab gesteigert werden.

Auf Basis der durch Sensordaten erzeugten Datentransparenz soll das bisherige Kunststoffrecycling durch drei Effekte verbessert werden: Erstens sollen durch die Datentransparenz positive Anreize für verbesserte Sammel- und Produktqualitäten und damit gesteigerte Rezyklatmengen und -qualitäten geschaffen werden. Zweitens sollen sensorbasiert erfasste Stoffstromcharakteristika dazu genutzt werden, Sortier-, Aufbereitungs- und Kunststoffverarbeitungsprozesse auf schwankende Stoffstromeigenschaften adaptieren zu können. Drittens soll die verbesserte Datenlage eine ganzheitliche ökologische und ökonomische Bewertung der Wertschöpfungskette ermöglichen.

An ReVise-UP beteiligen sich insgesamt 18 Forschungsinstitute, Verbände und Industriepartner. Das Bundesministerium für Bildung und Forschung (BMBF) fördert ReVise-UP im Rahmen der Förderrichtlinie „Ressourceneffiziente Kreislaufwirtschaft – Kunststoffrecyclingtechnologien (KuRT)” mit 3,92 Mio. €.

Weitere Informationen zu ReVise-UP finden sich unter: https://www.ants.rwth-aachen.de/cms/IAR/Forschung/Aktuelle-Forschungsprojekte/~bdueul/ReVise-UP/

Verbundpartner in ReVise-UP sind:

• Institut für Anthropogene Stoffkreisläufe der RWTH Aachen University (Konsortialleiter)
• Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
• Deutsches Institut für Normung e. V.
• Human Technology Center der RWTH Aachen University
• Hündgen Entsorgungs GmbH & Co. KG
• Krones AG
• Kunststoff Recycling Grünstadt GmbH
• SKZ – KFE gGmbH
• STADLER Anlagenbau GmbH
• Wuppertal Institut für Klima, Umwelt, Energie gGmbH
• PreZero Recycling Deutschland GmbH & Co. KG

Als assoziierte Partner wird ReVise-UP unterstützt von:

• bvse – Bundesverband Sekundärrohstoffe und Entsorgung e. V.
• cirplus GmbH
• HC Plastics GmbH
• Henkel AG
• Initiative „Mülltrennung wirkt“
• Procter & Gamble Service GmbH
• TOMRA Sorting GmbH

Kontakt: Dr. Jason Rambach , Dr. Bruno Mirbach

English version

Research project “ReVise-UP” started to improve the process efficiency of mechanical plastics recycling using sensor technology

In September 2023, the BMBF-funded research project ReVise-UP (“Improving the process efficiency of mechanical recycling of post-consumer plastic packaging waste through intelligent material flow management – implementation phase”) started. In the four-year implementation phase, the transparency and efficiency of mechanical plastics recycling is to be increased by developing and demonstrating sensor-based material flow characterization methods on an industrial scale.

Based on the data transparency generated by sensor data, the current plastics recycling shall be improved by three effects: First, data transparency is intended to create positive incentives for improved collection and product qualities and thus increased recyclate quantities and qualities. Second, sensor-based material flow characteristics are to be used to adapt sorting, treatment and plastics processing processes to fluctuating material flow properties. Third, the improved data situation should enable a holistic ecological and economic evaluation of the value chain.

A total of 18 research institutes, associations and industrial partners are participating in ReVise-UP. The German Federal Ministry of Education and Research (BMBF)v is funding ReVise-UP with €3.92 million as part of the funding guideline “Resource-efficient recycling management – plastics recycling technologies (KuRT)”.

More information about ReVise-UP can be found at: https://www.ants.rwth-aachen.de/cms/IAR/Forschung/Aktuelle-Forschungsprojekte/~bdueul/ReVise-UP/?lidx=1

Project partners in ReVise-UP are:

• Department of Anthropogenic Material Cycles Stoffkreisläufe of RWTH Aachen University (consortium lead)
• Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
• Deutsches Institut für Normung e. V.
• Human Technology Center of RWTH Aachen University
• Hündgen Entsorgungs GmbH & Co. KG
• Krones AG
• Kunststoff Recycling Grünstadt GmbH
• SKZ – KFE gGmbH
• STADLER Anlagenbau GmbH
• Wuppertal Institut für Klima, Umwelt, Energie gGmbH
• PreZero Recycling Deutschland GmbH & Co. KG

Associated partners in ReVise-UP are:

• bvse – Bundesverband Sekundärrohstoffe und Entsorgung e. V.
• cirplus GmbH
• HC Plastics GmbH
• Henkel AG
• Initiative „Mülltrennung wirkt“
• Procter & Gamble Service GmbH
• TOMRA Sorting GmbH

Contact: Dr. Jason Rambach , Dr. Bruno Mirbach

DFKI Augmented Vision researchers Praveen Nathan, Sandeep Inuganti, Yongzhi Su and Jason Rambach received their 1^st place award in the prestigious BOP Object Pose Estimation Challenge 2023 in the categories Overall Best RGB Method, Overall Best Segmentation Method and The Best BlenderProc-Trained Segmentation Method.

The BOP benchmark and challenge addresses the problem of 6-degree-of-freedom object pose estimation, which is of great importance for many applications such as robot grasping or augmented reality. This year, the BOP challenge was held within the “8th International Workshop on Recovering 6D Object Pose (R6D)” http://cmp.felk.cvut.cz/sixd/workshop_2023/  at the International Conference on Computer Vision (ICCV) in Paris, France https://iccv2023.thecvf.com/  .

The awards were received by Yongzhi Su and Dr. Jason Rambach on behalf of the DFKI Team and a short presentation of the method followed. The winning method was based on the CVPR 2022 paper “ZebraPose”  

The winning approach was developed by a team led by DFKI AV, with contributing researchers from Zhejiang University.

List of contributing researchers:

DFKI Augmented Vision: Praveen Nathan, Sandeep Inuganti, Yongzhi Su, Didier Stricker, Jason Rambach

Zhejiang University:  Yongliang Lin, Yu Zhang

On the 5^th and 6^th of September 2023, the new EU project dAIEdge “A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge“ officially took off.

The kick-off meeting held at DFKI in Kaiserslautern was an excellent occasion to meet with the 36 partners from 15 European countries and launch the activities of the network!

The main goal of dAIEDGE is to support and ensure the rapid development and market adoption of distributed edge AI technologies, such as hardware, software, frameworks, and tools.

The applications of dAIEDGE will be used in a wide range of domains, such as the Internet of Things (IoT), intelligent transportation systems, satellite imagery and robotics.

The network has a project volume of €14.4 million, of which €10.7 million is funded by the European Union. Looking forward to a fruitful collaboration and a successful project!

Contact persons:

Dr. Alain Pagani

Dr. Mohamed Selim

DFKI Augmented Vision is collaborating with Stellantis on the topic of Radar-Camera Fusion for Automotive Object Detection using Deep Learning. Recently, two new publications were accepted to the GCPR 2023 and EUSIPCO 2023 conferences.

The 2 new publications are:

1.  Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird’s-Eye View,  Proceedings of the 31st. European Signal Processing Conference (EUSIPCO-2023), September 4-8, Helsinki, Finland, IEEE, 2023.

Lukas Stefan Stäcker, Philipp Heidenreich, Jason Rambach, Didier Stricker

This paper investigates the influence of the training dataset and transfer learning on camera-radar fusion approaches, showing that while the camera branch needs large and diverse training data, the radar branch benefits more from a high-performance radar.

Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird’s-Eye View

2. RC-BEVFusion: A Plug-In Module for Radar-Camera Bird’s Eye View Feature Fusion,  Proceedings of. Annual Symposium of the German Association for Pattern Recognition (DAGM-2023), September 19-22, Heidelberg, BW, Germany, DAGM, 9/2023.

Lukas Stefan Stäcker, Shashank Mishra, Philipp Heidenreich, Jason Rambach, Didier Stricker

This paper introduces a new Bird’s Eye view fusion network architecture for camera-radar fusion for 3D object detection that performs favorably on the NuScenes dataset benchmark.

RC-BEVFusion: A Plug-In Module for Radar-Camera Bird’s Eye View Feature Fusion

Contacts: Dr. Jason Rambach

We are happy to announce that the Augmented Vision group will present 4 papers in the upcoming ICCV 2023 Conference, 2-6 October, Paris, France. The IEEE/CVF International Conference in Computer Vision (ICCV) is the premier international computer vision event. Homepage: https://iccv2023.thecvf.com/  

The 4 accepted papers are:

1. U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
   Yan Di, Chenyangguang Zhang, Ruida Zhang, Fabian Manhardt, Yongzhi Su, Jason Raphael Rambach, Didier Stricker, Xiangyang Ji, Federico Tombari
2. FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision. Khurram Azeem Hashmi, Goutham Kallempudi, Didier Stricker, Muhammad Zeshan Afzal
3. Introducing Language Guidance in Prompt-based Continual Learning Muhammad Gulzain Ali Khan, Muhammad Ferjad Naeem; Luc Van Gool; Federico  Tombari; Didier Stricker, Muhammad Zeshan Afzal
4. DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport Sk Aziz Ali, Djamila Aouada, Gerd Reis, Didier Stricker