基于深度置信网络的实木板材缺陷及纹理识别研究

针对在现代木材加工企业中，实木板材以缺陷及纹理为主要品质分级要素的需求，提出利用基于局部二值模式、自学习的深度置信网络与softmax分类器组合的深度学习算法，实现对实木板材缺陷及纹理的分类。首先提取实木板材的缺陷及纹理特征，在此基础上利用深度置信网络对经过局部二值化处理的特征进行训练学习，并采用可自学习的学习率算法优化收敛速度、减少训练时间，最后使用softmax分类器获取常见缺陷及直纹、花纹的分类结果。通过与BP神经网络、支持向量机、极限学习机等几种经典算法的比较，采用深度置信网络得到的实木板材缺陷及纹理识别的误差率在3.59%左右，在实木板材缺陷和纹理上取得了更好的识别效果。

Research on defects and textures recognition of solid wood lumbers based on deep belief network

In modern wood processing enterprises, the main quality classification factors of solid wood lumbers are defects and textures. This research proposed a deep learning algorithm based on a mixture of local binary pattern, self-learning deep belief networks and softmax classification to classify solid wood lumbers with defects and textures. Firstly, it extracted the defects and textures features of solid wood lumbers, on which to be based, used the deep belief networks to train and learn the characteristics of the local binarized processing. Afterwards, it adopted the self-learning learning rate algorithm to optimize the convergence speed and reduced the training time. Finally, the algorithm obtained the common defects, straight textures and confused textures by using a softmax classification. Compared with classical algorithms e. g. BP neural network, support vector machine and extreme learning machine, the error rate of solid wood defects and textures recognition obtained by the deep belief networks designed is about 3.59%, and this algorithm works efficiently in recognition the defects and textures of solid wood lumbers.