Authors:
Christoph Praschl
1
and
Gerald Adam Zwettler
2
;
1
Affiliations:
1
Research Group Advanced Information Systems and Technology (AIST), University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
;
2
Department of Software Engineering, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
Keyword(s):
Instance Localization, Segmentation, Classification, Wooden Piles, Logs, Cross Faces, Deep Learning, Neural Networks.
Abstract:
The inspection of products and the assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows the analysis automation of wooden piles in a vision-based manner. In this work a three-step approach is presented for the localization, segmentation and multi-facet classification of individual logs based on a client/server architecture allowing to determine the quality, volume and like this the value of a wooden pile based on a smartphone application. Using multiple YOLOv4 and U-NET models leads to a client-side log localization accuracy of 82.9% with low storage requirements of 23 MB and a server-side log detection accuracy of 94.1%, together with a log type classification accuracy of 95% and 96% according to the quality assessment of spruce logs. In addition, the trained segmentation model reaches an accuracy of 89%.