卷积神经网络在储粮害虫图像识别中的应用研究

立足于当今储粮害虫图像识别领域面临的技术需求,针对现有的储粮害虫图像识别算法网络结构相对复杂,辨认率低,为此,引入卷积神经网络实现储粮害虫图像的识别。简要阐述了卷积神经网络发展过程,分析其网络结构,选用5种储粮害虫作为训练样本,分析了储粮害虫图像识别过程,最后通过实验得出了基于卷积神经网络的Alexnet模型对储粮害虫图像识别的精确度达97.62%,说明基于CNN对储粮害虫图像识别具有较高的准确率。

Application of convolutional neural network in image recognition of stored grain insects

Based on the technical requirement in the field of stored-grain insect image recognition nowadays, aiming at the complex network structure and low recognition rate of the existing stored-grain insect image recognition algorithm, convolutional neural network is introduced to realize the image recognition of stored-grain insect. The development process of convolutional neural network is briefly introduced, its network structure is analyzed. Five kinds of stored-grain insects are selected as training samples. The process of image recognition of stored-grain insects is analyzed. The Alexnet model based on convolutional neural network is obtained by the test, which accuracy reaches to 97.62%. It shows that the image recognition of stored grain insects based on CNN has higher accuracy rate.