基于卷积神经网络的花生籽粒完整性识别算法及应用

针对现有色选设备在花生颗粒筛选过程中处理速度慢、准确率低的缺点，提出基于卷积神经网络的花生籽粒完整性识别算法。以完好花生、表皮破损花生和果仁破损花生的分类为例，构建花生图像库；搭造卷积神经网络，提取花生图像特征；为提高分类准确率和实时性，从训练集构成、减小过拟合、加快训练收敛速度、简化网络结构等几方面对卷积神经网络进行优化；最终利用含2个卷积层、2个池化层、2个全连接层的3层神经网络实现了上述3类花生的分类。试验结果表明：该方法对花生分类的准确率达到98.18%，平均检测一幅单粒花生图像的时间为18 ms，与现有色选设备相比有效提高了色选设备筛选的准确率和实时性。

Identification algorithm and application of peanut kernel integrity based on convolution neural network

Abstract:Aiming at the shortcomings of the existing color sorter machine for crop sorting, such as slow processing speed, low accuracy, and the dependence on experience value, a granular crop integrity identification algorithm based on convolutional neural network was proposed. Taking the classification of intact peanuts, skin damaged peanuts and half peanuts as instance, the three types of peanut images were acquired. After comparing the filtering effects of mean filtering, median filtering and Gaussian filtering, median filtering was adopted for image preprocessing. 407 effective peanut images were divided into the above three categories and manually labeled. Then the images were divided into training sets and validation sets, and the above three types of peanut pictures in the training set and the validation set were evenly distributed. A convolutional neural network with 4 convolutional layers, 4 pooling layers and 3 fully connected layers was built to extract the peanut image features. The accuracy of testing peanut classification on the CPU(central processing unit) platform combined GPU(graphics processing unit) was 90.91%. In contrast, the classification accuracy of the traditional BP neural network was 85.45%. It could be seen that the convolutional neural network algorithm constructed in this paper effectively improved the accuracy of granular crop recognition. In order to further improve the accuracy and real-time performance of the classification algorithm, it was necessary to optimize the established convolutional neural network. Over-fitting referred to the fact that when a model was overly complex, it could "memorize" the portion of random noise in each training data and forgot to "learn" the tendencyof the training data. In this paper, the regularization method was used to reduce the over-fitting, and the experimental results of L1 regularization and L2 regularization were compared. It was proved that the L2 regularization on the data set effectively improved the classification accuracy and reduced the over-fitting. In the process of training, the neural network used the back propagation algorithm, namely gradient descent and chain derivation rule, to optimize the neural network. The learning rate was an important parameter in the gradient descent algorithm. In this paper, the exponential decay method was used to set the learning rate. Firstly, a large learning rate was used to quickly obtain a better solution. Then, as the iteration continued, the learning rate was gradually reduced, making the model more stable in the later stage of training. The accuracy increase was larger, the latter was smaller, and the overall improvement was better than that before optimization, and the expected effect was achieved. In this paper, the moving average model was used to reduce the influence of noise in the training data on the model, and the training convergence speed was accelerated. The experiment proved that the accuracy fluctuation was reduced and the model stability was enhanced. Since the algorithm needed to be applied to the color sorting system, real-time judgment and processing of the materials on the conveyor belt required high real-time performance. Considering that the image information of peanut was relatively simple, the network structure could be simplified to improve the real-time performance. The simplified convolutional neural network consisted of 2 convolutional layers, 2 pooling layers, and 2 fully connected layers. The final optimization scheme included L2 norm regularization, exponential decay learning rate, moving average model and simplified network structure. The accuracy of optimized classification algorithm applied on the peanut data set was 98.18%, and the average processing time for detecting one peanut image was 18.3 ms, which demonstrated that the optimized convolutional neural network significantly improved the classification accuracy and real-time performance. The research work in this paper showed that the application of deep learning in the crop sorting field was feasible and effective.