【摘要】 为了实现电路板板载元器件的非接触及快速准确识别,提出了一种基于卷积神经网络的电路板元器件分类算法(C-CNN)。将工业相机拍摄的电路板图像进行直方图均衡化,减少光照不均产生的影响。提取监督样本用于训练卷积神经网络,并创建分类模型。将待测图像输入深度卷积神经网络进行特征提取,并利用分类模型对电路板元器件进行识别。利用实际拍摄的电路板板载元器件图像数据集验证文章提出的算法,实验结果表明:C-CNN能够对元器件进行识别,并且性能优于支持向量机及深度神经网络算法。更多还原

【Abstract】 In order to implement non-contact and fast and accurate identification of circuit board components, a classification algorithm of circuit board components based on convolution neural network(C-CNN) is proposed in this paper. Firstly, histogram equalization of circuit board images taken by industrial cameras is used to reduce the impact of uneven illumination. Supervisory samples are extracted to train convolutional neural networks, and a classification model is created. Then, the image under detection is input into the depth convolution neural network for feature extraction, and the classification model is used to identify the circuit board components. The experimental results show that C-CNN can recognize components, and its performance is better than that of support vector machine and deep neural network.更多还原