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.

翻译：手动操作因其灵活性和低实施成本，在工业生产中仍然不可或缺。然而，确保其质量并实时监控执行过程仍是一个挑战，尤其是在高变异性和人为错误存在的条件下。本文提出了一种基于人工智能的控制系统，用于追踪手动装配过程，并提出了一种新颖的方法来评估其整体效率。所开发的系统包含一个多摄像头设置和一个基于YOLOv8的检测模块，该模块集成在一个旨在模拟真实生产场景的实验台中。评估方法依赖于预测执行阶段与实际执行阶段在时间戳级别上的比较，使用了三个关键指标：交并比（IoU）、平均绝对比例误差（MASE）以及残差分布直方图。这些指标被汇总为一个统一的效率指数E_total，用于可重复的系统评估。所提出的方法在一个包含120次以不同速度执行的装配任务的数据集上进行了验证，结果表明系统具有较高的分割精度，并能识别出特定阶段的时序偏差。结果证实了该控制系统的鲁棒性，以及该评估框架在工业环境中对类似解决方案进行基准测试的适用性。