基于深度神经网络的液体视觉识别研究

针对液体表面特征少，区分度低，机器视觉难以有效识别检测的问题，通过使用两束不同波长的激光光源同时照射液体来提高不同液体之间的区分度，设计了数据集自动采集装置为模型训练提供了大量有效的样本，并构建了基于EfficientNetV2深度神经网络的视觉识别模型，模型引入cosine学习率衰减，调节获得最佳超参数后，形成最优方式实现高效训练，进一步提升了预测精度，结果表明视觉检测系统能够获得100%的测试准确率，成功解决了液体视觉检测中特征少的难题。

Research on liquid vision recognition based on deep neural network

Aiming at the problem that liquid surface features are few and discrimination is low, which is difficult to be recognized and detected effectively by machine vision, two laser light sources with different wavelengths are used to irradiate liquid at the same time to improve the discrimination between different liquids. An automatic collection device of data set is designed to provide a large number of effective samples for model training and then a visual recognition model based on EfficientNetV2 deep neural network was constructed. After introducing cosine learning rate dacay into the model and regulating super-parameters to the best, the optimal method was formed to realize efficient training, and the prediction accuracy was further improved. The results showed that the visual detection system could obtain 100% prediction accuracy, and successfully solved the problem of few features in liquid visual detection.