The Image Classification of Workstation Instructions Using Convolutional Neural Networks

Halikowski, Daniel; Patalas‐Maliszewska, Justyna

doi:10.1007/978-3-030-30604-5_27

Cited by 1 publication

(2 citation statements)

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“…The process of image recognition and analysis is closely related to the process of video analysis, understood as a sequence of individual images. The probability of belonging to a class is calculated on the basis of the features extracted from individual frames [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Processing units are grouped into layers, creating subsequent layers (e.g., convolutional). This sort of network relies on the natural, stationary property of an image, i.e., the statistics of one part of the image are the same as any other part and information extracted at one part of the image can also be employed in other parts [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

Patalas‐Maliszewska

Halikowski

2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%