基于剪枝决策树的人造板表面缺陷识别

连续压机生产线的发展，使人造板实现自动化生产，但缺陷检测环节仍为人工.缺陷识别是检测中的一个重要环节，是根据缺陷特征值利用分类器进行识别的过程.由于人造板连续生产，实时性要求高，为实现缺陷的快速、准确识别，提出了一种基于剪枝的CART树对人造板进行缺陷识别.通过对已有的人造板缺陷图像进行预处理、分割，获得缺陷的形状、纹理特征作为输入，通过基于Gini指数的CART算法生成CART树.针对于自由生长的CART树容易出现过拟合的问题，利用代价复杂度算法对生成的CART树进行剪枝，通过十折交叉验证对剪枝前后的子树进行比较，获得最优子树.通过实验证明剪枝后的CART树缺陷识别正确率高达93%，满足人造板缺陷识别的实时性和正确率的要求，可以实现人造板在线缺陷检测.

Defect Recognition of Wood-Based Panel Surface Using Pruning Decision Tree

The automatic production of wood-based panel has been realized with the development of continuous press production line, but the defect detection is still manual. As an important part of detection, defect recognition is a process of using a classifier to identify defects based on feature value. For the reason of the continuous production of wood-based panels, the defects need to be identified quickly and accurately. Therefore, a cart tree is proposed to identify the defects of the wood-based panel in this study. The defect features of shape and texture are firstly obtained using image preprocessing and image segmentation, and then the cart tree is generated by Gini exponent, at last defects are identified by using the cart tree. But it is easy to cause the problem of overfitting using cart tree without pruning, so the study obtains the optimal subtree by using the cost complexity algorithm and 10 cross-validations. The experimental results reflect that the accuracy rate of defect recognition reaches 93% with the proposed cart tree, which can satisfy the requirements of real-time and accuracy on defect identification.