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Automatic detection of elevator controls using image processing
Černil, Martin ; Šnajder, Jan (referee) ; Krejsa, Jiří (advisor)
This thesis deals with the automatic detection of elevator controls in personal elevators through digital imaging using computer vision. The theoretical part of the thesis goes through methods of image processing with regards to object detection in image and research of previous solutions. This leads to investigation into the field of convolutional neural networks. The practical part covers the creation of elevator controls image dataset, selection, training and evaluation of the used models and the implementation of a robust algorithm utilizing the detection of elevator controls. The conclussion of the work discusses the suitability of the detection on given task.
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Mobile Robot in the Elevator: What Floor Am i On?
Krejsa, Jiří ; Věchet, Stanislav ; Chen, K.S. ; Havelka, M. ; Černil, M.
Wheeled mobile robots in multiple stories buildings have to use elevator to access arbitrary floor of the building. To do so, the control system must be able to detect and access elevator controls and also determine on which floor the elevator stopped. The paper deals with the latter problem, using the fusion of relative floor change detected by onboard accelerometer and absolute floor number detected by the processing of elevator information screen panel image acquired by onboard camera. Bayesian filter is used for data fusion and convolution neural network for image processing. Field tests resulted in 97% correct detection.
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Automatic detection of elevator controls using image processing
Černil, Martin ; Šnajder, Jan (referee) ; Krejsa, Jiří (advisor)
This thesis deals with the automatic detection of elevator controls in personal elevators through digital imaging using computer vision. The theoretical part of the thesis goes through methods of image processing with regards to object detection in image and research of previous solutions. This leads to investigation into the field of convolutional neural networks. The practical part covers the creation of elevator controls image dataset, selection, training and evaluation of the used models and the implementation of a robust algorithm utilizing the detection of elevator controls. The conclussion of the work discusses the suitability of the detection on given task.
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