Generation of grinding area for vehicle surface maintenance based on convex hull algorithm

GENG Fangqi; WU Minghui; WANG Yaqiang; ZHOU Zhifeng; ZHOU Wei

doi:10.12299/jsues.23-0069

Volume 37 Issue 4

Dec. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(4): 380-386

GENG Fangqi, WU Minghui, WANG Yaqiang, ZHOU Zhifeng, ZHOU Wei. Generation of grinding area for vehicle surface maintenance based on convex hull algorithm[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 380-386. doi: 10.12299/jsues.23-0069

Citation:

GENG Fangqi, WU Minghui, WANG Yaqiang, ZHOU Zhifeng, ZHOU Wei. Generation of grinding area for vehicle surface maintenance based on convex hull algorithm[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 380-386. doi: 10.12299/jsues.23-0069

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Generation of grinding area for vehicle surface maintenance based on convex hull algorithm

doi: 10.12299/jsues.23-0069

1.
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Military Representative Office of Army Armaments Department in Xiangtan, Xiangtan 411100, China

Received Date: 2023-03-12
Publish Date: 2023-12-30

Abstract

Abstract

In a traditional industry, automotive paint repair is dominated by manual labor, which has problems such as high labor intensity, low efficiency and poor consistency. Combining the experience of manual sanding, the algorithm of generating the damaged car surface repair sanding area was designed based on the extraction of the damaged car surface contour. Firstly, the edge detection operator and convex hull algorithm were used to extract the contour of the damaged paint surface, then the convex package algorithm was used to pre-generate the contour, and then the contour was improved to generate the final car surface repair sanding area. By comparing the actual manual sanding area and the algorithm-generated sanding area, it shows that the sanding area generated by the algorithm is more accurate and meets the basic sanding requirements, which can provide a theoretical basis for the realization of automated sanding.
- automotive paint repair,
- convex hull algorithm,
- sanding area generation

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References(9)

References

[1]	NGENDANGENZWA B. Defect detection and classification on painted specular surfaces [D]. Umeå: Umeå University, 2018.
[2]	AKHTAR S, TANDIYA A, MOUSSA M, et al. An efficient automotive paint defect detection system[J] . Advances in Science, Technology and Engineering Systems Journal,2019,4(3):171 − 182.
[3]	ZHOU Q B, CHEN R W, HUANG B, et al. Deep Inspection: Deep leaning based hierarchical network for specular surface inspection[J] . Measurement,2020,160:107834.
[4]	韩双旺, 崔兆顺, 鲍丽红, 等. 基于目标图像的提取与测量[J] . 上海工程技术大学学报,2007,21(4):345 − 350. doi: 10.3969/j.issn.1009-444X.2007.04.015
[5]	廖鸿飞, 李鸿燕, 徐才恩, 等. 基于方向梯度的边缘检测算法及实现[J] . 上海工程技术大学学报,2017,31(1):16 − 19. doi: 10.3969/j.issn.1009-444X.2017.01.004
[6]	BOUATTANE O, ELMESBAHI J, RAMI A. A fast parallel algorithm for convex hull problem of multi-leveled images[J] . Journal of Intelligent and Robotic Systems,2002,33(3):285 − 299.
[7]	ASGHAR H J, LI S J, PIEPRZYK J, et al. Cryptanalysis of the convex hull click hum an identification protocol [C]//Proceedings of the 13th International Conference on Information Security. Boca Raton: Springer, 2010: 24−30.
[8]	包振华. 凸集及凸包形态学运算的几个性质[J] . 科学技术与工程,2003(1):69 − 71. doi: 10.3969/j.issn.1671-1815.2003.01.023
[9]	陈尹刚. 基于数学形态学图像处理算法研究[J] . 信息通信,2019(12):67 − 68.