Significant advancements in the field of wood species identification are
needed worldwide to support sustainable timber trade. In this work we
contribute to automate the identification of wood species via high-resolution
macroscopic images of timber. The main challenge of this problem is that
fine-grained patterns in timber are crucial in order to accurately identify
wood species, and these patterns are not properly learned by traditional
convolutional neural networks (CNNs) trained on low/medium resolution images.
We propose a Timber Deep Learning Identification with Patch-based Inference
Voting methodology, abbreviated TDLI-PIV methodology. Our proposal exploits the
concept of patching and the availability of high-resolution macroscopic images
of timber in order to overcome the inherent challenges that CNNs face in timber
identification. The TDLI-PIV methodology is able to capture fine-grained
patterns in timber and, moreover, boosts robustness and prediction accuracy via
a collaborative voting inference process.
In this work we also introduce a new data set of marcroscopic images of
timber, called GOIMAI-Phase-I, which has been obtained using optical
magnification in order to capture fine-grained details, which contrasts to the
other datasets that are publicly available. More concretely, images in
GOIMAI-Phase-I are taken with a smartphone with a 24x magnifying lens attached
to the camera. Our data set contains 2120 images of timber and covers 37
legally protected wood species.
Our experiments have assessed the performance of the TDLI-PIV methodology,
involving the comparison with other methodologies available in the literature,
exploration of data augmentation methods and the effect that the dataset size
has on the accuracy of TDLI-PIV.

本文提出了一种基于基于高分辨率巨观木材图像的深度学习识别方法 (TDLI-PIV methodology)，旨在自动化木材物种识别，克服了传统卷积神经网络在木材识别中面临的挑战。此方法能够捕捉木材的细粒度纹理，并通过协同投票推理过程提高鲁棒性和预测准确性。

利用高分辨率宏观图像的深度学习方法识别木材种类

Deep Learning methodology for the identification of wood species using  high-resolution macroscopic images

Timber and forest products made from wood, like furniture, are valuable
commodities, and like the global trade of many highly-valued natural resources,
face challenges of corruption, fraud, and illegal harvesting. These grey and
black market activities in the wood and forest products sector are not limited
to the countries where the wood was harvested, but extend throughout the global
supply chain and have been tied to illicit financial flows, like trade-based
money laundering, document fraud, species mislabeling, and other illegal
activities. The task of finding such fraudulent activities using trade data, in
the absence of ground truth, can be modelled as an unsupervised anomaly
detection problem. However existing approaches suffer from certain shortcomings
in their applicability towards large scale trade data. Trade data is
heterogeneous, with both categorical and numerical attributes in a tabular
format. The overall challenge lies in the complexity, volume and velocity of
data, with large number of entities and lack of ground truth labels. To
mitigate these, we propose a novel unsupervised anomaly detection --
Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is
generally applicable for large-scale heterogeneous tabular data. We demonstrate
our model CHAD performs favorably against multiple comparable baselines for
public benchmark datasets, and outperforms them in the case of trade data. More
importantly we demonstrate our approach reduces assumptions and efforts
required hyperparameter tuning, which is a key challenging aspect in an
unsupervised training paradigm. Specifically, our overarching objective
pertains to detecting suspicious timber shipments and patterns using Bill of
Lading trade record data. Detecting anomalous transactions in shipment records
can enable further investigation by government agencies and supply chain
constituents.

使用大规模异构表格数据的对比学习型异常检测（CHAD）模型可以帮助检测到可疑的木材运输和模式，从而使政府机构和供应链参与者能够进一步调查异常的运输记录并打击非法砍伐。