Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

• Published in: Advances in Multimedia Vol. 2022; pp. 1 - 11
• Main Authors: Chen, Xihua, Yang, Xing
• Format: Journal Article
• Language: English
• Published: New York Hindawi 30.09.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Arrays; Artificial neural networks; Chemical reactions; Color; Colorimetry; Corporate image; Feature extraction; Image processing; Image segmentation; Inspection; Neural networks; Object recognition; Optical properties; Physical properties; Semantics; Sensor arrays; Sensors
• ISSN: 1687-5680; 1687-5699; 1687-5699
• DOI: 10.1155/2022/4325117

The inspection of some substances usually includes two levels. One is the detection of the physical properties of the substance, which can be carried out through a series of physical detection methods and corresponding physical experiments. In the process of chemical detection, the color change after a chemical reaction is an extremely identifying optical property feature. In today’s increasingly mature Internet technology and related computer technology, the combination of this important identification chemical reaction and the former makes the chemical detection method visualized. The biggest difficulty in the application of this technology is to divide the color units produced by the chemical reaction in the contrast color sensor, which directly affects the identification process of the chemical reaction in the subsequent process. In order to better solve this problem, this paper will use a deep convolutional neural network to process the segmentation process of color units. And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. They achieve 79.11%, 76.13%, 77.61%, 91.11% 92.31%, 91.05%, 91.03%, and 91.03%, respectively, and the extraction rate for feature 3 at layer 4 reaches 96.19%. It can be known from the above results that the segmented part of the image generated by the colorimetric sensor array processed by the deep convolutional neural network will be more conducive to the final color unit identification.