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AI сontrol vs human mistakes: Enforcing efficiency of industrial operations and evaluation methodology

human-computer-interaction
artificial-intelligence
computer-vision
data-science
neural-networks

Abstract

Manual operations remain essential in industrial production because of their flexibility and low implementation cost. However, ensuring their quality and monitoring execution in real time remains a challenge, especially under conditions of high variability and human-induced errors. In this paper, we present an AI-based control system for tracking manual assembly and propose a novel methodology to evaluate its overall efficiency. The developed system includes a multicamera setup and a YOLOv8-based detection module integrated into an experimental stand designed to replicate real production scenarios. The evaluation methodology relies on timestamp-level comparisons between predicted and actual execution stages, using three key metrics: Intersection over Union (IoU), Mean Absolute Scaled Error (MASE), and Residual Distribution histograms. These metrics are aggregated into a unified efficiency index (Etotal ) for reproducible system assessment. The proposed approach was validated on a dataset of 120 assemblies performed at different speeds, demonstrating high segmentation accuracy and identifying stage-specific timing deviations. The results confirm the robustness of the control system and the applicability of the evaluation framework to benchmark similar solutions in industrial settings.
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