Chemical Image Recognition Technology And Application Based On Artificial Intelligence

With the continuous application and development of artificial intelligence technology in the industrial field,computer vision technology has developed rapidly,and many industries have become increasingly intelligent and digital.Nowadays,the amount of data and images generated in the chemical production process is huge,and using manual recognition and processing of these data is costly and inefficient.This article proposes a chemical image recognition technology based on artificial intelligence for research.The topic of the paper has a clear industrial background and has certain theoretical significance or practical value.Therefore,this article proposes a chemical data recognition technology model and method based on artificial intelligence,aiming to solve the problems of large chemical data,difficulty in visual recognition,and large errors in manual data reading.The specific research content includes:(1)Firstly,this study aims to solve the problems of complex backgrounds and low quality in chemical images based on image preprocessing technology,and proposes a chemical image recognition model based on artificial intelligence algorithms.In the preprocessing stage,grayscale processing,Gaussian denoising,and Canny edge detection were performed on the obtained image to binarize it.Subsequently,the optimized OTSU algorithm was used for image segmentation and mask mask was used to extract functional features,laying the foundation for further data analysis,data mining,and establishing a large-scale chemical database.(2)This article applies a Chemical KL-Net image classification model based on deep learning to solve the problem of the same function category.Relying on the excellent learning ability of machine vision,a new image classification model is established.This model designs a new type of envelope unit,which includes convolution and channel shuffling,and incorporates a CBAM attention mechanism in the unit,which can weight and extract key features.In addition,Leaky Re Lu function is introduced as the activation function model,and a loss function is constructed using the label smoothing function to reduce the error and impact of sample imbalance.The experimental results show that the Chemical KL-Net model achieved an accuracy of81.27% in the validation set,demonstrating excellent classification performance.The model maintains lightweight while possessing efficient learning ability,providing an effective solution for image classification.(3)Use the model established in this study to identify the function of the Perry manual’s centrifugal pump characteristic diagram and the influence diagram of solvent concentration on the activity coefficient of key components,read the data,and compare it with manually read data.The experimental results show that the average error rate of the recognition model proposed in this study on the test dataset is 9.81%.This modeling avoids tedious operations and can improve the efficiency of data extraction and recognition,which has certain practical value.