Log-End Cut-Area Detection in Images Taken from Rear End of Eucalyptus Timber Trucks

Abstract

The visual estimation of log volume and size distribution of eucalyptus logs on a truck is a challenging task. In Thailand, inspectors at paper mills typically perform this task. The information is used to determine whether the logs pass the criteria for the mill and to find the appropriate price. This method is far from accurate and not efficient. This paper presents a new approach to automatically detects eucalyptus logend cut area from rear-end images of eucalyptus timber trucks. The method used machine learning and image processing techniques. It consists of three parts: timber truck detection, log segmentation, and log counting. The proposed system was tested with 300 images of timber truck dataset and achieved an average accuracy of 94.45% in log segmentation and 2.71% of false negative.