@inproceedings{BundschererSchmittBocklet2024, author = {Bundscherer, Maximilian and Schmitt, Thomas and Bocklet, Tobias}, title = {Machine Learning in Industrial Quality Control of Glass Bottle Prints}, doi = {10.48550/arXiv.2409.20132}, pages = {8}, year = {2024}, abstract = {In industrial manufacturing of glass bottles, quality control of bottle prints is necessary as numerous factors can negatively affect the printing process. Even minor defects in the bottle prints must be detected despite reflections in the glass or manufacturing-related deviations. In cooperation with our medium-sized industrial partner, two ML-based approaches for quality control of these bottle prints were developed and evaluated, which can also be used in this challenging scenario. Our first approach utilized different filters to supress reflections (e.g. Sobel or Canny) and image quality metrics for image comparison (e.g. MSE or SSIM) as features for different supervised classification models (e.g. SVM or k-Neighbors), which resulted in an accuracy of 84\%. The images were aligned based on the ORB algorithm, which allowed us to estimate the rotations of the prints, which may serve as an indicator for anomalies in the manufacturing process. In our second approach, we fine-tuned different pre-trained CNN models (e.g. ResNet or VGG) for binary classification, which resulted in an accuracy of 87\%. Utilizing Grad-Cam on our fine-tuned ResNet-34, we were able to localize and visualize frequently defective bottle print regions. This method allowed us to provide insights that could be used to optimize the actual manufacturing process. This paper also describes our general approach and the challenges we encountered in practice with data collection during ongoing production, unsupervised preselection, and labeling.}, language = {en} } @techreport{SchmittBundschererBocklet2024, author = {Schmitt, Thomas and Bundscherer, Maximilian and Bocklet, Tobias}, title = {Semmeldetector: Application of Machine Learning in Commercial Bakeries}, doi = {10.48550/arXiv.2406.04050}, pages = {6}, year = {2024}, abstract = {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.}, language = {en} } @article{SchmittBundschererBocklet2024, author = {Schmitt, Thomas and Bundscherer, Maximilian and Bocklet, Tobias}, title = {Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images}, doi = {10.48550/arXiv.2409.20122}, pages = {10}, year = {2024}, abstract = {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.}, language = {en} }