College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2021 Jan 6;21(2):336. doi: 10.3390/s21020336.
Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.
实木地板具有良好的美学属性，是室内装饰的优秀材料。为了满足特定室内装饰要求的艺术效果，实木地板的颜色需要协调。因此，需要对生产的实木地板进行分类，以满足客户的个性化需求。在这项工作中，引入了机器视觉、深度学习方法和集成学习方法，以降低人工分类的成本，提高生产效率。使用彩色 CCD 相机采集了公司提供的三个颜色等级的 108 块实木地板，获得了 108 个 18000×2048 像素的木材图像。经过数据扩充后，共得到 432 张图像。比较了深度学习方法，如 VGG16、DenseNet121 和 XGBoost。使用 XGBoost 进行特征筛选后，实木地板颜色分类的准确率为 97.22%，训练模型的时间为 5.27s，每张图片的平均测试时间为 51ms，取得了较好的效果。
