Abstract
In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.
Highlights
Nowadays, large shopping malls, supermarkets, and small retail stores offer a wide variety of goods for consumers to choose, which brings rich and convenient shopping experience to people and greatly stimulates and promotes the speed of social development
Too et al (2019) constructed a blade image recognition system based on deep convolutional neural network, and the results show that the recognition accuracy of this method is up to 99.75% [5]
In order to evaluate the effect of convolution kernel size and Dropout rate on the recognition accuracy of multi-stage convolutional neural network (MS-convolutional neural network (CNN)) model, in this study, MS-CNN model is trained using the self-constructed commodity image database
Summary
Large shopping malls, supermarkets, and small retail stores offer a wide variety of goods for consumers to choose, which brings rich and convenient shopping experience to people and greatly stimulates and promotes the speed of social development. Barcode technology was mainly used to identify commodities. It needs to identify each commodity with barcode printed on the outer packaging. The printed bar codes of different commodities are not the same, so it is necessary to find the location of bar codes manually to assist machine recognition, so the degree of automation is relatively low [2,3]. In recent years, automated retail stores have appeared successively at home and abroad. This store uses artificial intelligence technology to realize automated and unmanned sales of goods. In order to liberate productivity, it is very important to use image vision technology to identify goods
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