Shelf Commodity Identification Method Based on Hybrid Fully Convolutional Automatic Encoder

At present, the semantic information segmentation algorithms mainly include FCN (Fully Convolutional Network), PSPNet (Pyramid Scene Parsing Network), Deeplab and so on. In view of the inadequate results of features extracted by these algorithms from RGB image, a hybrid fully convolutional autoencoder neural network (HFCAN) structure, which introduces fully convolutional neural network and stacked sparse autoencoder, is proposed in this paper. Using the FCN to generate the thermal high-dimensional feature map of the shelf commodity, and then performing the up-sampling operation on the segmented feature map. During the up-sampling operation, the convolution features are refined by the stacked sparse autoencoder (SAE), and the image boundary details are retained, so that the classification results are more accurate. The experimental results show that the hybrid fully convolutional autoencoder model proposed in this paper can not only shorten the training time and testing time of neural network by nearly 50%, but also improve the accuracy of shelf commodity identification by nearly 95%.