Real-Time KenalKayu System with YOLOv3

Rosli, N.R. and Khairuddin, U. and Fathi, M.F.N. and Khairuddin, A.S.M. and Ahmad, A. (2021) Real-Time KenalKayu System with YOLOv3. Advances in Intelligent Systems and Computing, 1350 A. pp. 224-232. ISSN 21945357, DOI

Full text not available from this repository.
Official URL:


An automated tropical wood species recognition system known as KenalKayu has been developed by the Centre for Artificial Intelligence & Robotics (CAIRO) to identify the tropical wood species. The system works very well in offline mode with an accuracy rate of up to 98. But when it comes to real-time testing, the accuracy rate dropped by about 62, partly due to low image quality. The system was trained by using ideal quality of wood images that are stored in the database. However, during real-time testing, the quality of wood image captured might be degraded due to motion blur, out of focus and illumination. Therefore, it is challenging to perform accurate recognition via real-time approach. This research proposed an improved KenalKayu prototype by using You Only Look Once version 3 (YOLOv3) algorithm to detect and classify tropical wood species via real-time approach. 60 images from 10 tropical wood species have been trained while another 60 images have been captured and tested during real-time testing. The preliminary test shows promising results where the system is now able to classify tropical wood species in real-time mode with accuracy rate for both training set and testing set are 100. The average accuracy rate for output probability generated by YOLOv3 is 95.63. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia (Grant No. 5F265)
Uncontrolled Keywords: Deep learning; Image analysis; Pattern recognition; YOLOv3
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 20 Dec 2023 12:35
Last Modified: 20 Dec 2023 12:35

Actions (login required)

View Item View Item