Abstract
The paper describes the experiments with the use of deep neural networks (CNN) for robust identification of assembly parts (screws, nuts) and assembly features (holes), to speed up any assembly process with augmented reality application. The simple image processing tasks with static camera and recognized parts can be handled by standard image processing algorithms (threshold, Hough line/circle detection and contour detection), but the augmented reality devices require dynamic recognition of features detected in various distances and angles. The problem can be solved by deep learning CNN which is robust to orientation, scale and in cases when element is not fully visible. We tested two pretrained CNN models Mobilenet V1 and SSD Fast RCNN Inception V2 SSD extension have been tested to detect exact position. The results obtained were very promising in comparison to standard image processing techniques.
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Acknowledgement
This work was supported by the Agency for Research and Development under the contract no. APVV-15-0602.
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Židek, K., Hosovsky, A., Piteľ, J., Bednár, S. (2019). Recognition of Assembly Parts by Convolutional Neural Networks. In: Hloch, S., Klichová, D., Krolczyk, G., Chattopadhyaya, S., Ruppenthalová, L. (eds) Advances in Manufacturing Engineering and Materials. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-99353-9_30
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DOI: https://doi.org/10.1007/978-3-319-99353-9_30
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