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 https://doi.org/10.1007/978-3-030-70917-4_22.
Full text not available from this repository.Abstract
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 |
| URI: | http://eprints.um.edu.my/id/eprint/35905 |
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