Institute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland.
Faculty of Technical Science, University of Applied Science in Nysa, 48-300 Nysa, Poland.
Sensors (Basel). 2020 Apr 30;20(9):2571. doi: 10.3390/s20092571.
Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model's accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee's activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.
如今，在制造公司中，有必要验证新员工所进行的服务工作的准确性。研究中发现，缺乏有效的自动评估新员工工作的方法。本研究的主要目的是构建一个新的深度学习模型，以便自动评估单个工人的活动。所提出的方法集成了称为 CNN、CNN + SVM、CNN + R-CNN 的方法，以及四个新算法和来自选定公司的一项工作，将其用作自创数据集，以创建一种能够评估单个工人活动的解决方案。数据是从没有任何指导或脚本工作的运行制造单元中收集的。结果表明，所开发的模型能够准确地检测工作过程的正确性。该模型的准确性在很大程度上超过了当前制造中用于检测工作活动的最先进方法。所提出的两阶段方法，首先使用 CNN 为给定员工的活动分配适当的图形指令，然后使用 R-CNN 从参考帧中隔离对象，分别产生 94.01%和 73.15%的识别准确率。
