# Determination of efficiency indicators of the stand for intelligent control of manual operations in industrial production

arXiv: https://arxiv.org/abs/2401.10777 · HTML: https://mlsanity.com/paper/2401.10777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [IoU Loss for 2D/3D Object Detection](https://mlsanity.com/paper/1908.03851/) (2019)
- [Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data](https://mlsanity.com/paper/1810.10093/) (2018)
- [Automatic dataset generation for specific object detection](https://mlsanity.com/paper/2207.07867/) (2022)

## Abstract (arXiv)

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), Residual Distribution histograms. These metrics are aggregated into a unified efficiency index E_total 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.