倒置残差网络结合注意力机制的掺假羊肉分类检测系统构建  

Construction of the detection system for mutton adulteration classification based on inverted residual network and convolutional block attention module

作　　者：何东宇 朱荣光[1,2] 范彬彬 王世昌 崔晓敏 姚雪东 He Dongyu;Zhu Rongguang;Fan Binbin;Wang Shichang;Cui Xiaomin;Yao Xuedong(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China;Key Laboratory of Northwest Agricultural Equipment,Ministry of Agriculture and Rural Affairs,Shihezi 832003,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832003 [2]农业农村部西北农业装备重点实验室,石河子832003

出　　处：《农业工程学报》2022年第20期266-275,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金地区科学基金项目(31860465);兵团中青年科技创新领军人才计划项目(2020CB016)。

摘　　要：针对羊肉精和染色剂作用下的猪肉掺假羊肉分类检测问题,该研究提出并建立了一种检测速度较快、精度较高的注意力机制结合倒置残差网络模型,同时基于智能手机开发了对应的快速、准确检测应用软件。首先,对羊肉、不同部位猪肉、不同掺假比例下的猪肉掺假羊肉的原始手机图像,使用数据增强方式进行数据扩充;其次,用倒置残差结构替换残差网络框架中的原有残差结构,以减少网络参数量并加快模型收敛速度,同时,引入注意力机制(Convolutional Block Attention Module,CBAM),利用空间和通道特征对特征权重再分配,以强化掺假羊肉和羊肉之间的特征差异;然后,利用提出的注意力机制结合倒置残差网络(CBAM-Invert-ResNet)对样本进行训练并确定模型参数;最后,将训练好的网络模型移植到智能手机,以实现掺假羊肉的移动端检测。研究结果表明:与ResNet50和CBAM-ResNet50相比,Invert-ResNet50、CBAM-Invert-ResNet50模型的参数量分别减少了58.25%和61.64%,模型大小分别减小了58.43%和61.59%;针对背脊、前腿、后腿和混合部位数据集,CBAM-Invert-ResNet50模型验证集的分类准确率分别为95.19%、94.29%、95.81%、92.96%;把建立的网络模型部署到移动端后,每张图片的检测时间约为0.3 s。该研究可实现对羊肉精和染色剂作用下的不同部位猪肉掺假羊肉的移动端快速、准确分类检测,可为维护市场秩序和保护食品安全提供技术支持。Accurate and real-time detection of meat adulteration has been an ever-increasing high demand in the food industry in recent years. However, the presence of mutton flavor essence and dye can make the detection more difficult than before. In this study, a residual network(Res Net) model was proposed to classify the mutton adulteration using Convolutional Block Attention Module(CBAM) combined with the inverted residual(Invert). Meanwhile, an application software was also developed to realize the rapid and accurate classification using smart phones. Firstly, the original images were collected from the mutton, three parts pork, and adulterated mutton using a mobile phone. Hough circle detection was then used to remove the background of the images. Data augmentation(such as rotation, offset, and mirroring) was used to expand the sample images.6800 images were acquired, two-thirds of which were used as the training and testing dataset. Furthermore, the training dataset was three times larger than the testing one. The rest was then used as the independent validation dataset. Secondly, the original residual structure of the Res Net framework was replaced by the Invert structure, in order to reduce the number of network parameters for the high convergence speed. At the same time, the CBAM was introduced into the Invert structure. As such, the feature difference was strengthened to redistribute the feature weights in the spatial and channel. The convolution neural network(CBAM-Invert-ResNet) was then developed using the sample data. Furthermore, the MobileNet and resnet50 were also developed using the same data to compare the convergence speed and accuracy of the model. Finally, the CBAM-Invert-ResNet network model was transplanted to mobile phones by the TensorFlow Lite framework and Android Studio development environment. The mobile terminal classification was realized in real time. The results showed that the CBAM greatly enhanced the feature difference among categories, whereas, the Invert significantly reduced the par

关 键 词：图像处理 深度学习 羊肉掺假 注意力机制 倒置残差 智能手机 羊肉精 染色剂 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TS251.53[自动化与计算机技术—计算机科学与技术]