Semmeldetector: Application of Machine Learning in Commercial Bakeries
This work addresses production optimization and resource efficiency for commercial bakers, but it is incremental as it applies existing methods to a new domain.
The researchers tackled the problem of tracking unsold baked goods in commercial bakeries by developing an object detection system called Semmeldetector, which achieved an AP@0.5 of 89.1% on a test set of 1151 images covering 18 types of baked goods.
The Semmeldetector, is a machine learning application that utilizes object detection models to detect, classify and count baked goods in images. Our application allows commercial bakers to track unsold baked goods, which allows them to optimize production and increase resource efficiency. We compiled a dataset comprising 1151 images that distinguishes between 18 different types of baked goods to train our detection models. To facilitate model training, we used a Copy-Paste augmentation pipeline to expand our dataset. We trained the state-of-the-art object detection model YOLOv8 on our detection task. We tested the impact of different training data, model scale, and online image augmentation pipelines on model performance. Our overall best performing model, achieved an AP@0.5 of 89.1% on our test set. Based on our results, we conclude that machine learning can be a valuable tool even for unforeseen industries like bakeries, even with very limited datasets.