Glue detection based on teaching points constraint and tracking model of pixel convolution

Lei Geng; Xiao Ma; Zhitao Xiao; Wen Wang

doi:10.1117/12.2288628

12 January 2018 Glue detection based on teaching points constraint and tracking model of pixel convolution

Lei Geng, Xiao Ma, Zhitao Xiao, Wen Wang

Proceedings Volume 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems; 106210Y (2018) https://doi.org/10.1117/12.2288628
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

Abstract

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Citation Download Citation

Lei Geng, Xiao Ma, Zhitao Xiao, and Wen Wang "Glue detection based on teaching points constraint and tracking model of pixel convolution", Proc. SPIE 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 106210Y (12 January 2018); https://doi.org/10.1117/12.2288628

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