  The availability of large image data sets has been a crucial factor in the
success of deep learning-based classification and detection methods. While data
sets for everyday objects are widely available, data for specific industrial
use-cases (e.g. identifying packaged products in a warehouse) remains scarce.
In such cases, the data sets have to be created from scratch, placing a crucial
bottleneck on the deployment of deep learning techniques in industrial
applications.
  We present work carried out in collaboration with a leading UK online
supermarket, with the aim of creating a computer vision system capable of
detecting and identifying unique supermarket products in a warehouse setting.
To this end, we demonstrate a framework for using synthetic data to create an
end-to-end deep learning pipeline, beginning with real-world objects and
culminating in a trained model.
  Our method is based on the generation of a synthetic dataset from 3D models
obtained by applying photogrammetry techniques to real-world objects. Using
100k synthetic images generated from 60 real images per class, an InceptionV3
convolutional neural network (CNN) was trained, which achieved classification
accuracy of 95.8% on a separately acquired test set of real supermarket product
images. The image generation process supports automatic pixel annotation. This
eliminates the prohibitively expensive manual annotation typically required for
detection tasks. Based on this readily available data, a one-stage RetinaNet
detector was trained on the synthetic, annotated images to produce a detector
that can accurately localize and classify the specimen products in real-time.
