Coarse annotation refinement for segmentation of dot-matrix batchcodes

Jia, Ning; Holder, Chris; Bonner, Stephen; Obara, Boguslaw

Abstract

Deep Convolutional Neural Networks (CNN) have been extensively applied in various computer vision tasks. Although such approaches have demonstrated exceptionally high performance in various open challenges, adapting them to more specialised tasks can be non-trivial. In this paper we discuss our design and implementation of a batchcode detection system capable of accurate segmentation of batchcode regions within images of consumer products. A batchcode is a unique identifier printed on the packaging of many products that encodes useful information such as date and location of manufacture. Detection of batchcodes in images of products is a useful step in many processes, including quality control, supply chain tracking and counterfeit detection. Beginning with a unique dataset of product images and a set of crowdsourced coarse annotations that roughly correspond to the locations of batchcodes, we demonstrate that such annotations are insufficient for training a reliable model, and subsequently describe a novel label refinement process, which we call the Maximally Stable Global Region (MSGR) method, that we use to generate accurate ground-truth data suitable for training a robust neural network. We also show that detection accuracy can be further improved by applying MSGR to the output of the neural network. We evaluate our approach using a manually labelled test dataset of images of shampoo bottles, and demonstrate the efficacy of the proposed method for accurate real-time batchcode detection.

Citation

Jia, N., Holder, C., Bonner, S., & Obara, B. (2019, December). Coarse annotation refinement for segmentation of dot-matrix batchcodes. Presented at IEEE International Conference on Machine Learning and Applications, Boca Raton, FL, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE International Conference on Machine Learning and Applications
• Start Date: Dec 16, 2019
• End Date: Dec 19, 2019
• Acceptance Date: Oct 7, 2019
• Online Publication Date: Feb 17, 2020
• Publication Date: 2019
• Deposit Date: Oct 7, 2019
• Publicly Available Date: Mar 19, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 2001-2007
• Book Title: Proceedings of the 18th IEEE International Conference on Machine Learning and Applications.
• DOI: https://doi.org/10.1109/icmla.2019.00320
• Public URL: https://durham-repository.worktribe.com/output/1141948