ABSTRACT

Using multivariate partial least squares regression (PLS) to perform visual quality grading of sawn timber requires a training set with known quality grades for the training of a grading model. This study evaluated the grading accuracy of an independent test set of sawn timber when changing the aspects of class-balance and class-overlap of the training set consisting of 251 planks. The study also compared two ways of expressing the reference-grade of the training set; by grading images picturing the planks, and by grading the product produced from the planks. Two grading models were trained using each reference-grade to establish a baseline for comparison. Both models achieved a 76% grading accuracy of the test set, indicating that both reference-grades can be used to train comparable models. To study the class-balance and class-overlap aspects of the training set, 25% of the training set was removed in two training scenarios. The models trained on class-balanced data indicated that class-imbalance of the training set was not a problem. The models trained on data with less class-overlap using the product-grade reference suffered a 4%-points grading accuracy loss due to the smaller training set, while the model trained using the image-grade reference retained its grading accuracy.

摘要

使用多元偏最小二乘回归（PLS）进行锯材的视觉质量评级需要使用具有已知质量等级的训练集来训练等级模型。这项研究评估了当改变由251块木板组成的训练集的班级平衡和班级重叠方面时，独立的锯材测试集的等级准确性。该研究还比较了两种表达训练参考等级的方法：通过对描绘木板的图像进行分级，以及对由木板生产的产品进行分级。使用每个参考等级训练了两个等级模型，以建立比较基准。两种模型均达到测试集76％的评分精度，表明这两种参考等级均可用于训练可比模型。为了研究培训集的班级平衡和班级重叠方面，在两种培训方案中删除了25％的培训集。使用班级平衡数据进行训练的模型表明，训练集的班级不平衡不是问题。由于使用了较小的训练集，使用产品等级参考以较少的类别重叠数据训练的模型遭受了4％点的等级精度损失，而使用图像等级参考训练的模型则保持了其等级精度。