Abstract
Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly. One possibility is to use a deep learning algorithm for instance-aware semantic segmentation. Such methods achieve high accuracies but require a large amount of annotated training data.
We propose a system to generate the training annotations in a weakly supervised manner, drastically reducing the labeling effort. We assume that for each training image, only the object class is known. The system automatically segments the corresponding object from the background. The obtained training data is augmented to simulate variations similar to those seen in real-world setups.
Our experiments show that with appropriate data augmentation, our approach obtains competitive results compared to a fully-supervised baseline, while drastically reducing the amount of manual labeling.
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Appendix
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In Tables 2, 3 and 4 we show the influence of augmenting a different amount of images and adding specific augmentations for baseline, weakly, and weakly cleaned, respectively. The performance is given in terms of mAP percentage values. Abbreviations for augmentation types are as follows: neighboring (NB), random background (RB), reflections (RE).
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Follmann, P., Drost, B., Böttger, T. (2019). Acquire, Augment, Segment and Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_25
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