Tic-tac defect detection is based on a use case we developed to automate pill production quality control and save a pharmaceutical company millions of euros per year. When a pill press breaks, it begins to produce defective pills that can take hours or sometimes days to recognize. With each pill press producing hundreds of pills per second, the monetary stakes of potentially losing days of product are high. To address the issue, we prototyped a (deep) machine learning pipeline using transfer learning to detect defective pills. One particular challenge was to avoid false positives because they lead to unnecessary and costly production disruptions. Some of the causes of false positives were noise in images captured under practical but imperfect conditions (e.g. dust, unwanted reflections, CCD shot noise, etc.). Our pretrained lower layers (InceptionV3) was not as effective as we needed it to be at extracting features that could differentiate dust from pill defects, and we did not have enough labelled data to retrain them. We instead show how pre-processing techniques with Gaussian mixtures models, auto-encoders, and non-local means can be used to improve the quality of our transfer learning based image recognition pipeline. Cloud platforms allowed the team to quickly and cheaply explore and compare a wide range of machine learning techniques, thereby leading to an effective working prototype within a short development cycle.



February 28, 2018