摘要
为解决AlexNet网络模型在中药饮片图像识别中存在的识别准确率和鲁棒性不够理想的问题,以常见的50种中药饮片为研究对象,对AlexNet网络模型进行改进优化。首先通过拍摄以及搜索引擎获取中药饮片图像,并对图像进行数据扩充以及细节增强预处理。其次对AlexNet网络模型进行优化改进,通过缩减原网络的卷积核个数和卷积核大小、使用全局平均池化(global average pooling,GAP)替代全连接层以减少网络参数;去除局部响应归一化(local response normalization,LRN)层、引入批量归一化(batch normalization,BN)层和使用Lion优化算法替代随机梯度下降(stochastic gradient descent,SGD)优化算法以提高网络训练速度;使用Mish激活函数替代ReLU激活函数和引入通道注意力机制SENet网络以提高模型的识别精度。实验结果表明,改进后的网络模型相比于AlexNet网络模型,平均识别率提高了6.1%,平均损失率下降了14.4%,网络参数由原来的60 M缩减至1 M,该结果表明在中药饮片数据集上,改进后的网络模型具有更高的识别率和更好的鲁棒性,可为中药饮片图像识别领域的进一步发展提供有力支持。
In order to address the issue of the AlexNet network model s insufficient recognition accuracy and robustness in Chinese herbal slice image recognition,a study was conducted to investigate the improvement and optimization of the AlexNet network model.Firstly,images of 50 common Chinese herbal slices were obtained through shooting and search engines,and the images underwent data expansion and detail enhancement preprocessing.Then,the AlexNet network model was optimized and improved by lowering the original network s convolutional kernel number and size.Global average pooling(GAP)was used instead of full connection layer to reduce network parameters.The local response normalization(LRN)layer was removed and the batch normalization(BN)layer was introduced.The Lion optimizer was used to replace the stochastic gradient descent(SGD)optimizer to improve network training speed.The Mish activation function was used in place of ReLU activation function,and the channel attention mechanism SENet network was introduced to enhance the recognition accuracy of the model.The experimental results show that compared to the AlexNet network model,the improved network model exhibits an average recognition rate increase of 6.1%,an average loss rate decrease of 14.4%and the network parameters have been reduced from the original 60 M to 1 M.It is concluded that the improved network model shows higher recognition accuracy and better robustness on the dataset of Chinese herbal slices,providing strong support for further development in the field of image recognition of Chinese herbal slices.
作者
李玥辰
赵晓
王若男
杨晨
LI Yue-chen;ZHAO Xiao;WANG Ruo-nan;YANG Chen(College of Electronic Information and Artificial Intelligence,Shaanxi University of Science&Technology,Xi'an 710021,China)
出处
《科学技术与工程》
北大核心
2024年第9期3596-3604,共9页
Science Technology and Engineering
基金
国家自然科学基金(61971272,61601271)
陕西科技大学博士启动经费(2019BJ-27)。
