Explosion of the world of computer vision, pave the way to visual recognition which is now extended to visual food and metadata recognition Physical activities disruption due to Covid-19 pandemic rapidly increases the online food order Online Customers relies on available good quality food images and metadata information, to make decision of the type of food to order Recently developed deep learning networks outperform the classical approaches for visual food recognition, however these networks thirsts for large datasets to achieve best performance Majority population of present online customers order food to small-scale restaurants, these restaurants deals with small datasets, thus restricted them to take advantage of modern tools and to participate in the billion-digital business In this work, we modified the existing deep-CNN architecture to fit the small-scale restaurant dataset and trained on an end-to-end DeepLab v3+ initialized from ResNet weight Our proposed novel architecture is designed by exploiting the output of multiscale contextual information of CNN encoder and fed in the low-level features of our constructed Resnet-18 as the backbone network, and finally fine-tuned with simple filters, and bilinear interpolation on the order factor by 4 This approach reduces the serious overfitting of the deep-CNN The metadata recognition was done using enhanced-OCR, where the segmented image was analyzed at high-level layers The accuracy of our method is reported using IoU and BF score The numerical validation of the method is carried out on ETH-food-101 dataset and it demonstrates compliance to the state-of-the-art performance © 2020, World Academy of Research in Science and Engineering All rights reserved
Semantic parsing for automatic retail food image recognition