  This paper presents an improved scheme for the generation and adaption of
synthetic images for the training of deep Convolutional Neural Networks(CNNs)
to perform the object detection task in smart vending machines. While
generating synthetic data has proved to be effective for complementing the
training data in supervised learning methods, challenges still exist for
generating virtual images which are similar to those of the complex real scenes
and minimizing redundant training data. To solve these problems, we consider
the simulation of cluttered objects placed in a virtual scene and the
wide-angle camera with distortions used to capture the whole scene in the data
generation process, and post-processed the generated images with a
elaborately-designed generative network to make them more similar to the real
images. Various experiments have been conducted to prove the efficiency of
using the generated virtual images to enhance the detection precision on
existing datasets with limited real training data and the generalization
ability of applying the trained network to datasets collected in new
environment.
