
Roberts Kadiķis is a senior researcher and head of Robotics and Machine Perception laboratory at EDI, where he works since 2011. The main research areas are artificial intelligence, computer vision, and machine learning, while specific interests include deep neural networks (convolutional neural networks CNN, recurrent neural networks RNN, generative adversarial networks GAN), the learning of meaningful embeddings, explainable AI, generation of synthetic training data, bio-inspired learning systems, and computationally efficient vision algorithms. The applied experience of using deep models and other image processing tools includes the development of object detection, tracking, and image segmentation methods for tasks in transportation, mobility, biomedicine, and industry. The efficiency of the object detection algorithms was the main emphasis of the PhD thesis that was defended in the field of electronics at Riga Technical University in 2018.
Recent projects
- Efficient module for automatic detection of people and vehicles using video surveillance cameras (VAPI) #ERDF
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Framework of key enabling technologies for safe and autonomous drones applications (COMP4DRONES) #H2020
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Vision, Identification, with Z-sensing Technology and key Applications (VIZTA) #H2020
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Artificial Intelligence for Digitizing Industry (AI4DI) #H2020
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Silicon IP Design House (SilHouse) #ESIF
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Programmable Systems for Intelligence in Automobiles (PRYSTINE) #H2020
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Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (VPP SOPHIS) #SRP (VPP)
- Innovative technologies for acquisition and processing of biomedical images (InBiT) #ESIF
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Multifunkcionāla inteliģenta transporta sistēmas punkta tehnoloģija (MITS) #ESIF
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Silicon IP Design House (SilHouse) part 2 #ESIF
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Intelligent Motion Control under Industry 4.E (IMOCO4.E) #H2020
- AI-improved organ on chip cultivation for personalised medicine (AImOOC) #H2020
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Augmenting and Evaluating the Physical and Digital Infrastructure for CCAM deployment (AUGMENTED CCAM) #H2020
Recent publications
- Lulla M., Rutkovskis A., Slavinska A., Vilde A., Gromova A., Ivanovs M., Skadins A., Kadikis R., Elsts A. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038
- Maksims Ivanovs, Roberts Kadiķis, Kaspars Ozols. 2021. “Perturbation-based methods for explaining deep neural networks: A survey” Elsevier B.V., https://doi.org/10.1016/j.patrec.2021.06.030
- Novickis, R., Levinskis, A., Kadiķis, R., Feščenko. V., Ozols, K. (2020). Functional architecture for autonomous driving and its implementation. 17th Biennial Baltic Electronics Conference (BEC2020), Tallinn, Estonia.
- Buls, E., Kadikis, R., Cacurs, R., & Ārents, J. (2019, March). Generation of synthetic training data for object detection in piles. In Eleventh International Conference on Machine Vision (ICMV 2018) (Vol. 11041, p. 110411Z)
- Rihards Novickis, Aleksandrs Levinskis, Vitalijs Fescenko, Roberts Kadikis, Kaspars Ozols, Anna Ryabokon, Rupert Schorn, Jochen Koszescha, Selim Solmaz, Georg Stettinger, Akwasi Adu-Kyere, Lauri Halla-aho, Ethiopia Nigussie, Jouni Isoaho. "Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project", Appl. Sci. 2022, 12(1), 168;
- Romans Maliks, Roberts Kadikis. Multispectral Data Classification with Deep CNN for Plastic Bottle Sorting. 2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2022, pp. 58-65, ISBN: 978-1-6654-0641-3
- Ivanovs, Maksims, Kaspars Ozols, Artis Dobrajs, and Roberts Kadikis. 2022. "Improving Semantic Segmentation of Urban Scenes for Self-Driving Cars with Synthetic Images" Sensors 22, no. 6: 2252. https://doi.org/10.3390/s22062252
- Arents, J., Lesser, B., Bizuns, A., Kadikis, R., Buls, E., Greitans, M. (2022). Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_59