Foreign Object Debris Detection on the Runway Based on Wavelet Method

Guo, Xingqi; Yang, Xue; Yu, Zhi Jing

doi:10.4028/www.scientific.net/amm.427-429.1658

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…The study [7] describes depending on image processing system, a major sub-system in foreign object debris (FOD) detecting system on the runway. The paper analyses characteristics and principles of wavelet transformation and applies the theory in image processing under poor visual background for identifying the FOD's shape and mark characteristic point on the runway.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of Coefficients for Reusable Continuous Wavelet Transform

Khamukhin

Khamukhin²

2014

AMR

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The division into two stages of the Continuous Wavelet Transform (CWT) computing is proposed. This is expedient in circumstances when CWT is repeated many times, e.g., for online detection of nonstationary signal singularities. It is shown that the preprocessing of wavelet coefficients in the first stage can significantly reduce computing time required in the second stage. The comparative estimation of the runtime reduction in the second stage of CWT calculation is deduced.

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Section: Introductionmentioning

confidence: 99%

Preprocessing of Coefficients for Reusable Continuous Wavelet Transform

Khamukhin

Khamukhin²

2014

AMR

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A Statistical Approach to Evaluation and Selection of Wavelets for Detecting Computer Attacks

Kotenko,

Saenko,

Bortniker

2024

Lecture Notes in Networks and Systems

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Machine learning-enhanced vision systems for cutting tool notch detection in new energy battery manufacturing

Zheng,

Wang,

Chen

et al. 2024

Meas. Sci. Technol.

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This paper presents a study on the problem of burrs on the electrodes of new energy batteries, which are a major factor contributing to battery short-circuits and explosions. During the process of electrode cutting, the use of cutting tools with a notch is likely to cause burrs on the electrode. Therefore, it is essential to accurately detect the notch of the cutting tool. This paper explores the issue of cutting tool notch detection using machine learning-enhanced vision systems. Firstly, a set of cutting tool image acquisition devices is used to capture high-quality images of the cutting tool edge. Next, an algorithm for removing attachments based on concave point matching is proposed, effectively eliminating edge attachments by analyzing the concave point information of the edge. Additionally, we propose an enhanced Zernike moment sub-pixel edge extraction method, which achieves sub-pixel edge extraction while preserving the edge characteristics of the cutting tool. Furthermore, a notch detection algorithm based on quartic Hermite interpolation is introduced to detect the notch of the cutting tool by initially identifying the tool's edge. The proposed algorithms are compared with other state-of-the-art methods, demonstrating faster and more accurate extraction of sub-pixel cutting tool edges and detection of cutting tool notches.

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Foreign Object Debris Detection on the Runway Based on Wavelet Method

Cited by 4 publications

References 2 publications

Preprocessing of Coefficients for Reusable Continuous Wavelet Transform

Preprocessing of Coefficients for Reusable Continuous Wavelet Transform

A Statistical Approach to Evaluation and Selection of Wavelets for Detecting Computer Attacks

Machine learning-enhanced vision systems for cutting tool notch detection in new energy battery manufacturing

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