Mr. Hai-bin Sun Profile

Hai-bin Sun

at Unit 96871 of the Chinese People's Liberation Army

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Proceedings Article | 8 December 2020 Paper

Comparison of four visual tracking algorithms based on deep learning

Wei Jin, Hai-bin Sun, Xin Du, Hao-jin Li, Lin-run Ye, Kun Liu

Proceedings Volume 11606, 116060B (2020) https://doi.org/10.1117/12.2586820

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To evaluate the visual tracking algorithm proposed by our research team, we compare the algorithm with other three visual tracking algorithms. Firstly, the four visual tracking algorithms are introduced. There are SiamFC, SiamRPN++, ATOM and TDLD, which are all based on deep learning. The first three algorithms are the state-of-the-art trackers of different periods. The last algorithm is proposed by ourselves. And then we do some experiments in seven video sequences from OTB-100 dataset. We qualitatively compare the robustness of the four algorithms on the five tracking challenging factors. The average centre location error (ACLE) and average overlap score (AOC) of the four algorithms are calculated to make a quantitative analysis. The SiamRPN++ algorithm gets the best result of ACLE three times, and the TDLD gets twice. Both the SiamRPN++ and the TDLD get the best result of AOC three times respectively. The analysis results show that performance of the TDLD is very close to the state-of-the-art trackers.

Proceedings Article | 5 November 2020 Paper

A method for measuring the width of welding seam based on machine vision

Wei Jin, Hai-bin Sun

Proceedings Volume 11568, 1156816 (2020) https://doi.org/10.1117/12.2579886

KEYWORDS: Image segmentation, Machine vision, Image acquisition, Cameras, Software development, Light emitting diodes, Image enhancement, Evolutionary algorithms, Connectors

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Aiming at the problems of low measurement efficiency and large measurement error when manually measuring the width of the welding seam using the welding seam ruler, a method for measuring the width of the weld seam based on machine vision was proposed in this paper. Firstly, the hardware of the measuring device was designed. The hardware included a measurement platform, an image acquisition and control module, a computer. One motor guide, one stepper motor, an industrial camera and a lighting source were installed on the measurement platform. Secondly, based on Matlab + OpenCV, the measurement software was developed. The software included five functions: controlling the stepper motor and the lighting source, receiving weld seam images, preprocessing the images, segmenting images, correcting images and calculating the width of the weld seam. The image segmentation was implemented by GrabCut algorithm, which could extract the weld area very well. The calculation of the weld width was based on the imaging size and resolution of the industrial camera and the pixels positions on the upper and lower edges of the weld area. Finally, a measurement experiment was carried out on the width of the weld. According to the measurement results, the measurement accuracy of the width of the welding seam reached to 0.1 mm and the measurement speed reached to 30.7 mm / s. The results showed that measuring the width of the welding seam based on machine vision could replace manual measurement and be applied to industrial automation.

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