Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

Kumar, Rajeev; Singh, Jujhar; Sharma, Shubham; Li, Changhe; Królczyk, Grzegorz; Eldin, Sayed M.; Wojciechowski, Szymon

doi:10.1016/j.heliyon.2022.e12053

Cited by 6 publications

(2 citation statements)

References 77 publications

(75 reference statements)

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“…In view of the Shannon threshold value method being widely used in signal processing [46,47], two-dimensional maximum Shannon entropy can be utilized to select an optimal threshold value by calculating the two-dimensional gray-level histogram of the image coupled with maximum entropy theory [48]. As a result, it can be used to obtain better image processing effects, especially for noise images.…”

Section: Two-dimensional Maximum Shannon Entropymentioning

confidence: 99%

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Cao,

Liu

2024

Applied Sciences

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Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images.

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Section: Two-dimensional Maximum Shannon Entropymentioning

confidence: 99%

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Cao,

Liu

2024

Applied Sciences

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“…Spatial attention and channel attention were proposed by Fu et al 37 with promising results. Kumar et al 38 used Fourier Transform for object identification, localization and size estimation. Then, LAMA was proposed by Suvorov et al 39 to be effectively used for large mask inpainting.…”

Section: Contrast and Context Informationmentioning

confidence: 99%

MI2T-UNet: Integrating Multi-Scale Input Image and Top-hat Context for Infrared Small Object Segmentation

Shah

Khalil

Shah

et al. 2023

Preprint

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The segmentation of small objects in the infrared spectrum, commonly referred to as Infrared Small Object Segmentation (ISOS), holds significant importance in many applications, including but not limited to target detection, surveillance, and autonomous navigation. The task of achieving precise image segmentation of objects of interest is formidable, owing to the diminutive size of the objects in question, the intricate nature of the background, and the limited contrast differential. This research introduces a novel mobile-UNet architecture with attention gates, namely MI2T-UNet, which leverages multi-scale input images and top-hat operation. The proposed methodology involves the implementation of architectural modifications to improve the scale adaptation of the model. Specifically, these modifications include concatenating down-scaled input images with corresponding encoder layers, utilizing multi-kernel top-hats in the encoders and attention gates, and integrating encoder outputs with subsequent encoder layers to facilitate progressive information flow. This article also compares the proposed architecture against the top-performing models on the SIRST and IRSTD datasets, utilizing metrics for pixel level such as IoU/NIoU, object level including Pd/Fa, and model level comprising F-Score/AUC. The experimental results demonstrate that MI2T-UNet outperforms state-of-the-art models in terms of segmentation, with an average margin of over 1±0.25% for evaluation metrics. This research contributes to the ISOS field by proposing a novel approach to enhance the discriminating ability of the network. Specifically, a modified mobile-UNet has been developed, tailored to the ISOS requirements. The proposed approach has the potential to make significant strides in infrared image analysis and streamline various applications that hinge on precise ISOS.

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Fabrication and characterizations of glass fiber-reinforced functional leaf spring composites with or without microcapsule-based dicyclopentadiene as self-healing agent for automobile industrial applications: comparative analysis

Kumar

Sharma

Gulati

et al. 2023

Journal of Materials Research and Technology

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Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

Cited by 6 publications

References 77 publications

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

MI2T-UNet: Integrating Multi-Scale Input Image and Top-hat Context for Infrared Small Object Segmentation

Fabrication and characterizations of glass fiber-reinforced functional leaf spring composites with or without microcapsule-based dicyclopentadiene as self-healing agent for automobile industrial applications: comparative analysis

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