School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China.
PLoS One. 2020 Jul 7;15(7):e0235783. doi: 10.1371/journal.pone.0235783. eCollection 2020.
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 22 and 33, 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.
为了探索基于多阶段卷积神经网络（MS-CNN）的图像识别模型在深度学习神经网络中对商品图像的智能识别及其方法的识别性能的应用，在研究中，首先分析了商品图像的颜色、形状和纹理特征，分析了深度卷积神经网络（CNN）模型的基本结构。然后，构建了包含 50000 张不同商品的图片来验证模型的识别效果。最后，以 MS-CNN 模型为研究对象进行改进，探索不同参数设置和不同概率（卷积核大小、Dropout 率）下标签错误（p=0.03、0.05、0.07、0.09、0.12）对 MS-CNN 模型识别准确率的影响，同时构建基于 MS-CNN 模型的 CIR 系统平台，比较不同 SNR（0、0.03、0.05、0.07、0.1）椒盐噪声图像的识别性能，然后比较算法在实际图像识别测试中的性能。结果表明，当 MS-CNN 模型中的卷积核大小为 22 和 33 时，识别准确率最高（97.8%），当 Dropout 率为 0.1 时平均识别准确率最高（97.8%）；当图片标签的错误概率为 12%时，本研究构建的模型的识别准确率均在 96%以上。最后，利用本研究构建的商品图像数据库对模型进行识别验证，本研究算法在不同 SNR 条件下的识别准确率均明显高于 Minitch 随机梯度下降算法，且在 SNR=0 时（99.3%）识别准确率最高。试验结果表明，该模型在局部遮挡、不同视角、不同背景、不同光照强度等场景下对商品图像的识别效果较好，识别准确率为 97.1%。综上所述，本研究构建的基于 MS-CNN 模型的 CIR 平台具有较高的识别准确率和鲁棒性，为后续智能商品识别技术的实现奠定了基础。
