TY - CHAP A1 - Bundscherer, Maximilian A1 - Schmitt, Thomas A1 - Bocklet, Tobias T1 - Machine Learning in Industrial Quality Control of Glass Bottle Prints N2 - 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. KW - Machine Learning, Quality Control, Industrial Manufacturing, Glass Bottle Printsuring Optimization, Glass Printing Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2409.20132 ER - TY - RPRT A1 - Schmitt, Thomas A1 - Bundscherer, Maximilian A1 - Bocklet, Tobias T1 - Semmeldetector: Application of Machine Learning in Commercial Bakeries N2 - 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. KW - machine learning, object detection, YOLOv8, image composition, baked goods, food inspection, industrial automation Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2406.04050 ER - TY - JOUR A1 - Kammerbauer, Roland A1 - Schmitt, Thomas A1 - Bocklet, Tobias T1 - Segmenting Wood Rot using Computer Vision Models N2 - In the woodworking industry, a huge amount of effort has to be invested into the initial quality assessment of the raw material. In this study we present an AI model to detect, quantify and localize defects on wooden logs. This model aims to both automate the quality control process and provide a more consistent and reliable quality assessment. For this purpose a dataset of 1424 sample images of wood logs is created. A total of 5 annotators possessing different levels of expertise is involved in dataset creation. An inter-annotator agreement analysis is conducted to analyze the impact of expertise on the annotation task and to highlight subjective differences in annotator judgement. We explore, train and fine-tune the state-of-the-art InternImage and ONE-PEACE architectures for semantic segmentation. The best model created achieves an average IoU of 0.71, and shows detection and quantification capabilities close to the human annotators. KW - machine learning, image segmentation, semantic segmentation, InternImage, ONEPEACE, lumbering, industrial quality control, industrial automation Y1 - 2024 ER - TY - JOUR A1 - Schmitt, Thomas A1 - Bundscherer, Maximilian A1 - Bocklet, Tobias T1 - Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images N2 - 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. KW - machine learning, object detection, YOLOv9, image composition, baked goods, food industry, industrial automation Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2409.20122 ER - TY - CHAP A1 - Bundscherer, Maximilian A1 - Schmitt, Thomas A1 - Baumann, Ilja A1 - Bocklet, Tobias T1 - Digital Operating Mode Classification of Real-World Amateur Radio Transmissions T2 - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - This study presents an ML approach for classifying digital radio operating modes evaluated on real-world transmissions. We generated 98 different parameterized radio signals from 17 digital operating modes, transmitted each of them on the 70 cm (UHF) amateur radio band, and recorded our transmissions with two different architectures of SDR receivers. Three lightweight ML models were trained exclusively on spectrograms of limited non-transmitted signals with random characters as payloads. This training involved an online data augmentation pipeline to simulate various radio channel impairments. Our best model, EfficientNetB0, achieved an accuracy of 93.80% across the 17 operating modes and 85.47% across all 98 parameterized radio signals, evaluated on our real-world transmissions with Wikipedia articles as payloads. Furthermore, we analyzed the impact of varying signal durations & the number of FFT bins on classification, assessed the effectiveness of our simulated channel impairments, and tested our models across multiple simulated SNRs. KW - Automatic Modulation Classification KW - Amateur KW - Radio KW - Spectrum Monitoring KW - Cognitive Radio KW - Machine Learning Y1 - 2025 U6 - https://doi.org/10.1109/ICASSP49660.2025.10889837 SP - 1 EP - 5 PB - IEEE ER -