René Pihlak
René Pihlak
Tallinn University of Technology
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Pavement distress detection with deep learning using the orthoframes acquired by a mobile mapping system
A Riid, R Louk, R Pihlak, A Tepljakov, K Vassiljeva
Applied Sciences 9 (22), 4829, 2019
Pavement defect segmentation in orthoframes with a pipeline of three convolutional neural networks
R Lőuk, A Riid, R Pihlak, A Tepljakov
Algorithms 13 (8), 198, 2020
Deep learning for detection of pavement distress using nonideal photographic images
A Tepljakov, A Riid, R Pihlak, K Vassiljeva, E Petlenkov
2019 42nd International Conference on Telecommunications and Signal …, 2019
Morphological cross entropy loss for improved semantic segmentation of small and thin objects
R Pihlak, A Riid
Procedia Computer Science 192, 582-591, 2021
Simultaneous road edge and road surface markings detection using convolutional neural networks
R Pihlak, A Riid
International Baltic Conference on Databases and Information Systems, 109-121, 2020
Await: An ultra-lightweight soft-error mitigation mechanism for network-on-chip links
K Janson, R Pihlak, SP Azad, B Niazmand, G Jervan, J Raik
2018 13th International Symposium on Reconfigurable Communication-centric …, 2018
Smart elevator with unsupervised learning for visitor profiling and personalised destination prediction
M Leier, A Riid, T Alumäe, U Reinsalu, R Pihlak, A Udal, R Heinsar, ...
2021 IEEE Conference on Cognitive and Computational Aspects of Situation …, 2021
Identification of drivable road area from orthophotos using a convolutional neural network
A Riid, R Pihlak, R Liinev
2020 17th Biennial Baltic Electronics Conference (BEC), 1-5, 2020
Handling of SETs on NoC links by exploitation of inherent redundancy in circular input buffers
K Janson, R Pihlak, SP Azad, B Niazmand, G Jervan, J Raik
2018 16th Biennial Baltic Electronics Conference (BEC), 1-4, 2018
Automated Training Set Size Reduction for Detection of Small and Thin Objects
R Pihlak, A Riid
International Conference on Computing and Information Technology, 423-433, 2022
Vehicle Counting and Direction Determination with Convolutional Neural Network Using Data From a 9.9 GHz Low Power Microwave Radar
R Pihlak
Computer-Aided Design of Digital Systems (IAS0540) WEEK 6
R Pihlak
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