Tree Log Identity Matching Using Convolutional Correlation Networks

Abstract

Log identification is an important task in silviculture and forestry. It involves matching tree logs with each other and telling which of the known individuals a given specimen is. Forest harvesters can image the logs and assess their quality while cutting trees in the forest. Identification allows each log to be traced back to the location it was grown in and efficiently choosing logs of specific quality in the sawmill. In this paper, a deep two-stream convolutional neural network is used to measure the likelihood that a pair of images represents the same part of a log. The similarity between the images is assessed based on the cross-correlation of the convolutional feature maps at one or more levels of the network. The performance of the network is evaluated with two large datasets, containing either spruce or pine logs. The best architecture identifies correctly 99% of the test logs in the spruce dataset and 97% of the test logs in the pine dataset. The results show that the proposed model performs very well in relatively good conditions. The analysis forms a basis for future attempts to utilize deep networks for log identification in challenging real-world forestry applications.