Abstract
This paper presents a case study – developing a computer-based classification framework to classify masonry
bricks into three quality categories – carried out as a part of the Robocoast R&D Center project. The project aims at better
collaboration between universities and industry by establishing an innovation platform where companies can bring their challenges
to be addressed together with university experts. The project also promotes collaboration between universities being a part of the
RoboAI Competence Centre – a joint research and innovation platform of Satakunta University of Applied Sciences (SAMK)
and Tampere University, Pori unit. Automatic classification of bricks is important as it is foreseen that a robotic arm, powered by
an automatic classifier, could replace the heavy and tedious work currently performed by humans in brick factories. A
convolutional neural network-based solution, using a pretrained VGG-16 deep learning architecture, is proposed. Overall accuracy
of 88 % was obtained when considering all three quality classes.When only discarding class 3 bricks, i.e., those that are not suitable
for any construction work, the accuracy was 93 %.
bricks into three quality categories – carried out as a part of the Robocoast R&D Center project. The project aims at better
collaboration between universities and industry by establishing an innovation platform where companies can bring their challenges
to be addressed together with university experts. The project also promotes collaboration between universities being a part of the
RoboAI Competence Centre – a joint research and innovation platform of Satakunta University of Applied Sciences (SAMK)
and Tampere University, Pori unit. Automatic classification of bricks is important as it is foreseen that a robotic arm, powered by
an automatic classifier, could replace the heavy and tedious work currently performed by humans in brick factories. A
convolutional neural network-based solution, using a pretrained VGG-16 deep learning architecture, is proposed. Overall accuracy
of 88 % was obtained when considering all three quality classes.When only discarding class 3 bricks, i.e., those that are not suitable
for any construction work, the accuracy was 93 %.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Conference on Progress in Informatics and Computing (PIC) |
| Publisher | IEEE |
| Pages | 125-129 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-6654-2655-8, 978-1-6654-2654-1 |
| DOIs | |
| Publication status | Published - 19 Dec 2021 |
| Publication type | A4 Article in conference proceedings |
| Event | IEEE International Conference on Progress in Informatics and Computing - Online Duration: 17 Dec 2021 → 19 Dec 2021 http://www.picconf.com/ |
Conference
| Conference | IEEE International Conference on Progress in Informatics and Computing |
|---|---|
| Abbreviated title | PIC-2021 |
| Period | 17/12/21 → 19/12/21 |
| Internet address |
Keywords
- university-industry collaboration
- classification
- data acquisition
- CNN
- transfer learning
Publication forum classification
- Publication forum level 1