Fast Synthetic Dataset for Kitchen Object Segmentation in Deep Learning

Abstract

Object recognition has been widely investigated in computer vision for many years. Currently, this process is carried out through neural networks, but there are very few public datasets available with mask and class labels of the objects for the training process in usual applications. In this paper, we address the problem of fast generation of synthetic datasets to train neural models because creating a handcraft labeled dataset with object segmentation is a very tedious and time-consuming task. We propose an efficient method to generate a synthetic labeled dataset that adequately combines background images with foreground segmented objects. The synthetic images can be created automatically with random positioning of the objects or, alternatively, the method can produce realistic images by keeping the realism in the scales and positions of the objects. Then, we employ Mask-RCNN deep learning model, to detect and segment classes of kitchen objects using images. In the experimental evaluation, we study both synthetic datasets, automatic or realistic, and we compare the results. We analyze the performance with the most widely used indexes and check that the realistic synthetic dataset, quickly created through our method, can provide competitive results and accurately classify the different objects.