Case study: Real-time food inspection using deep learning

Jacob Zweig
Co-Founder, Principal Data Scientist

In a recent project, Strong worked with a leading food manufacturing and packaging company to automate their inspection process using computer vision and deep learning.

Building on tradition with the state-of-the-art

For over 100 years, our client had perfected a manual process that ensured the product going out the door was of the highest quality. Although painstaking, this process had earned the trust of stakeholders — something this new solution would have to earn as well.

Moreover, in applying state-of-the-art machine learning to this problem for the first time, we had to be mindful of important external considerations, including the fact that it would ultimately be deployed as an edge device in locations with poor remote connectivity.

Our solution

Strong Analytics designed, built, and deployed a deep-learning based computer vision solution built on Strong Vision to automatically detect defects in real-time from distributed food collection and grading sites throughout the world.

There were two major components. First, there was the hardware device that collected image data from multiple cameras in a portable form factor to allow on-site automated grading. Next, these data were streamed to a cloud-based analysis pipeline that integrated image data into coherent units and used deep learning to identify visible defects. Ultimately, data were summarized and then streamed to a data warehouse for further analysis and archival.

Critically, to ensure that we were as accurate as possible in defect analysis, our software enabled continuous 'human-in-the-loop' annotation of new data.