Digitizing Industrial Technical Layouts using Computer Vision and Machine Learning
Hameed, Umer (2021)
2021
Automaatiotekniikan DI-ohjelma - Master's Programme in Automation Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2021-12-27
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202112219485
https://urn.fi/URN:NBN:fi:tuni-202112219485
Tiivistelmä
Recently there have been rapid advancements in the field of image recognition. Researchers are looking for better ways to utilize the machine learning capabilities and advancements in computer vision to extract data from digital images. Industrial systems are moving towards automation and the use of computers and machines is ever increasing. An important aspect of ensuring personnel and product safety is the design and planning of industrial layouts. Technical layouts of various aspects of an industry need to be designed and implemented. These are traditionally not readable by machines and would require some form of digitizing.
This thesis focuses on the process of digitizing technical layouts into computer readable formats and useful data. The thesis proposes an approach to creating custom datasets that will be used to train a chosen model for object detection. The training and tuning of the model for object detection on custom dataset is implemented using a Jupyter notebook platform.
The proposed method was successfully implemented and tested on electrical layout. The results show that the model detects objects with high accuracy and speed. The thesis provides a guideline to develop similar techniques for various technical layouts.
This thesis focuses on the process of digitizing technical layouts into computer readable formats and useful data. The thesis proposes an approach to creating custom datasets that will be used to train a chosen model for object detection. The training and tuning of the model for object detection on custom dataset is implemented using a Jupyter notebook platform.
The proposed method was successfully implemented and tested on electrical layout. The results show that the model detects objects with high accuracy and speed. The thesis provides a guideline to develop similar techniques for various technical layouts.