Tomas Vantuch received his Ph.D. in Computer Science and Computation Technology at 2018 from VSB – Technical University of Ostrava, Czech Republic. His research interests focus on bioinspired and soft-computing methods, deep learning and their use in complex system analyses and predictions. Currently he is working as a data scientist for Stora Enso company and senior researcher at VSB-Technical University. He is an author of more than 30 academic publications (65+ citations) of various innovative approaches in computer science as well as several speaks at international conferences.
June 24, 2020 05:00 PM PT
In Storaenso, we keep track of our inventories very precisely with all the responsibility but because it is a quite time demanding task, we could not ignore the possibility of automation, even if there is only a partial chance. In our current situation, to calculate inventory amount of wood in our mill, means to fly over the entire area with a drone having a camera device, capture RGB image and ground level amplitude image of the area and store them on server. Later, an expert from the mill opens those images in our internally developed software and marks all wood piles â€“ based on those marks and ground level amplitude images, the total amount of wood is estimated. This is very precise and in deed much faster mechanism as it used to be, when the expert needed to come through all piles personally, which took him several hours of work.
To move this approach further, we developed an image processing pipeline, which is able to automatically identify all wood piles with very high precision. The main technologies that we are using are PyTorch, Azure databricks and open-cv. The multi-step pipeline incorporates two very known deep learning models â€“ ResNet for image classification producing a heat map of wood appearance probability and Unet for image segmentation to fine tune the borders of each wood pile. The final product of the mechanism is a binary array reflecting whether the current pixel represents wood pile or not. The models were trained on GPU provided by Azure databricks environment, which significantly increased the speed of the training and testing phases. High precision (over 97% in both nets) and stability of the model (wood of all shapes and colors is identified) is leading us towards the extension by multi-task machine learning approach.