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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Related Experiment Video

Updated: Mar 29, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Automated Detection of Quality Deviations in Poultry Processing Using Step-Specific YOLOv12 Models.

Daniel Einsiedel1,2,3, Marco Vita1, Florian Kaltenecker1,2

  • 1Department of Food Informatics, University of Hohenheim, 70599 Stuttgart, Germany.

Foods (Basel, Switzerland)
|March 28, 2026
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Summary

Artificial intelligence (AI) and computer vision (CV) can automate food quality control. A single AI model trained on all processing steps improved defect detection accuracy more than step-specific models.

Keywords:
computer visionfood processingfood qualityneural networksobject detection

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Area of Science:

  • Food Science and Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • Automated quality control in food manufacturing is crucial for consistency and safety.
  • Existing research often focuses on agricultural primary production, leaving gaps in processed food monitoring.
  • Object detection using computer vision (CV) presents a viable solution for in-line quality assessment.

Purpose of the Study:

  • To evaluate the effectiveness of object detection models for in-line quality monitoring in ready-to-eat chicken product manufacturing.
  • To compare the performance of step-specific AI models versus a single, multi-step model.
  • To identify challenges and potential improvements for CV-based quality control in food processing.

Main Methods:

  • Overhead cameras captured images across four processing steps: forming, coating, frying, and cooking.
  • 2000 images per step were labeled with multiple classes of quality deviations.
  • YOLOv12x object detection models were trained, both step-specific and a single model on a combined dataset, and evaluated using mAP50-95 and F1-curves.

Main Results:

  • Step-specific models achieved moderate peak mAP50-95 (0.50-0.60), with performance linked to class frequency.
  • Hyperparameter tuning offered limited gains despite significant computational cost.
  • A single model trained on combined data achieved a higher peak mAP50-95 (0.7331 ± 0.0040) and more balanced F1-curves, though with some loss of step-specific detection capabilities.

Conclusions:

  • Out-of-the-box object detection models offer practical value for industrial CV-enhanced food quality control.
  • Addressing class imbalance through targeted data collection for minority classes is key for future improvements.
  • Further enhancements require instance-centric datasets, higher resolution/multi-scale training, and advanced imbalance mitigation techniques.