A Comparative Study on Edge Detection Algorithms for Automotive Lighting Systems

Sen Ye; Mingge Sun; Jian Li; Dongxuan Huang; Jiaxuan Chai

doi:10.54097/prk9p067

Authors

Sen Ye
Mingge Sun
Jian Li
Dongxuan Huang
Jiaxuan Chai

DOI:

https://doi.org/10.54097/prk9p067

Keywords:

Canny Operator, Marr-Hildreth Operator, Edge Detection, Headlamp Inspection, Image Processing

Abstract

To address the challenges of edge blurring, noise interference, and insufficient adaptability of conventional algorithms in automotive headlamp inspection, this study proposes an optimized edge detection method integrating the Canny and Marr-Hildreth operators. Leveraging Gaussian filtering and a dynamic weighting fusion strategy, the proposed approach combines the gradient sensitivity of the Canny operator with the second-order differential characteristics of the Marr-Hildreth operator, effectively balancing detail preservation and noise suppression. Experimental results demonstrate that the improved algorithm significantly reduces edge localization errors in low-beam images compared to traditional methods, meeting the engineering requirements for automotive safety inspection.

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References

[1] Sobel I. Camera models and machine perception[R]. Computer Science Department, Technion, 1972.

[2] Prewitt J M S. Object enhancement and extraction[J]. Picture processing and Psychopictorics,1970, 10(1): 15-19.

[3] Canny J. A computational approach to edge detection[J]. IEEE Transactions on pattern analysis and machine intelligence, 1986 (6): 679-698. DOI: https://doi.org/10.1109/TPAMI.1986.4767851

[4] LEP Q, ILIYASU A M, DONG F, et al. Fast Geometric Transformations on Quantum Images [J]. IAENG International Journal of Applied Mathematics, 2010, 40(3): 113-123.

[5] JIANG N, WU W Y, WANG L, et al. Quantum Image Pseudo Color Coding Based on the Density-Stratified Method [J]. Quantum Information Processing, 2015, 14(5) : 1735-1755. DOI: https://doi.org/10.1007/s11128-015-0986-0

[6] ZHANG Y, LU K, XU K, et al. Local Feature Point Extraction for Quantum Images [J]. Quantum Information Processing, 2015, 14(5): 1573-1588. DOI: https://doi.org/10.1007/s11128-014-0842-7

[7] ZHANG Y, LU K, GAO Y H. QSobel: A Novel Quantum Image Edge Extraction Algorithm [J]. Science China (Information Sciences), 2014, 58: 012106-1-012106-13. DOI: https://doi.org/10.1007/s11432-014-5158-9

[8] YAO X W, WANG H, LIAO Z, et al. Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment [J]. Physical Review X, 2017, 7(3): 1-14. DOI: https://doi.org/10.1103/PhysRevX.7.031041

[9] SIMONA C, VASILE I M. Image Segmentation on a Quantum Computer [J]. Quantum Information Processing, 2015, 14(5): 1693-1715. DOI: https://doi.org/10.1007/s11128-015-0932-1

[10] Sarangi S, Rath N P. Performance Analysis of Fuzzy-based Canny Edge Detector[C]//Proc. of ICCIMA’07. Sivakasi, India: IEEE Press, 2007. DOI: https://doi.org/10.1109/ICCIMA.2007.375