Two-stage CNN-based wood log recognition

TL;DR

This paper introduces a two-stage CNN approach for log recognition that improves accuracy over traditional methods, aiding in illegal logging prevention and log tracking.

Contribution

It presents a novel CNN-based segmentation and recognition pipeline using triplet loss, enhancing log identification accuracy.

Findings

Outperforms traditional log recognition methods

Effective CNN segmentation of log ends

High accuracy in log identification

Abstract

The proof of origin of logs is becoming increasingly important. In the context of Industry 4.0 and to combat illegal logging there is an increasing motivation to track each individual log. Our previous works in this field focused on log tracking using digital log end images based on methods inspired by fingerprint and iris-recognition. This work presents a convolutional neural network (CNN) based approach which comprises a CNN-based segmentation of the log end combined with a final CNN-based recognition of the segmented log end using the triplet loss function for CNN training. Results show that the proposed two-stage CNN-based approach outperforms traditional approaches.