DASSL: Dynamic, AI-assisted, Scalable System for Labelling Used Bottle Images

Abstract

To ensure sustainable consumption and production, one way is to reduce waste generation by increasing the reuse rate. We have been working with the bottle classification facility to enhance the efficiency and productivity. Many used bottles come in with unimaginable ways of dirty, defective conditions. To manage the sheer volume of used bottles, we create an AI-enabled, bottle classification system. However, it requires many labelled images for training to improve accuracy. Unfortunately, the traditional approach, having human label individual images, is very time consuming. Even worse, it is not effective for our dataset because conditions of used bottles are not well defined and studied. From our experiments, the human experts cannot agree on the same labelling for similar bottle conditions, especially when impurities or defects are not separable objects. For 42%-99% of images in certain subcategories, human experts assign different labels to bottles with similar conditions. With huge inconsistency in data labelling, it deteriorates the accuracy of our classification models. To alleviate this problem, we propose a Dynamic, AI-assisted, Scalable System for Labelling used bottle images, called DASSL. DASSL employs multiple algorithms to extract and/or quantize different features of used bottle images, and cluster the images into groups with the supervision of human. With DASSL, we can achieve labelling consistency and improve scalability by reducing the data labelling time by at least 10x. To enhance agility, we can dynamically adjust DASSL to adapt to changes of cleaning machines' capabilities or bottle demand.