Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images

  • In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection model YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP@0.5 of 90.3% on our test set.

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Author:Thomas Schmitt, Maximilian Bundscherer, Tobias BockletORCiD
DOI:https://doi.org/10.48550/arXiv.2409.20122
ArXiv Id:http://arxiv.org/abs/2409.20122v1
Document Type:Article
Language:English
Date of first Publication:2024/09/30
Release Date:2024/10/17
Tag:machine learning, object detection, YOLOv9, image composition, baked goods, food industry, industrial automation
Pagenumber:10
Konferenzangabe:FZI Workshop - Künstliche Intelligenz im Mittelstand (KI-KMU 2024)
institutes:Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
Licence (German):Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International
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