Universiti Teknologi Malaysia Institutional Repository

Automatic detection and counting of stacked eucalypt timber using the YOLOv8 model

Casas, Gianmarco Goycochea and Ismail, Zool Hilmi and Limeira, Mathaus Messias Coimbra and Lopes da Silva, Antonilmar Araújo and Leite, Helio Garcia (2023) Automatic detection and counting of stacked eucalypt timber using the YOLOv8 model. Forests, 14 (12). pp. 1-13. ISSN 1999-4907

[img] PDF
3MB

Official URL: http://dx.doi.org/10.3390/f14122369

Abstract

The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. The best results were achieved at epoch 261, with a precision of 0.814, recall of 0.812, and mAP50 of 0.844 on the training set and a precision of 0.778, recall of 0.798, and mAP50 of 0.839 on the validation set. The model’s generalization was tested on separate images, demonstrating robust detection and accurate counting. The model effectively identified roundwood that was widely spaced, scattered, and overlapping. However, when applied to stacked timber, the automatic counting was not very accurate, especially when using images. In contrast, when using video, the relative percentage error for automatic counting significantly decreased to -12.442%. In conclusion, video proved to be more effective than images for counting stacked timber, while photographs should be reserved for the counting of individual roundwood pieces.

Item Type:Article
Uncontrolled Keywords:automated counting, computer vision, deep learning, forestry industry, object detection
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:107162
Deposited By: Widya Wahid
Deposited On:27 Aug 2024 08:22
Last Modified:27 Aug 2024 08:22

Repository Staff Only: item control page