Mohd Annuar Mohd Isa
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Mohd Annuar Mohd Isa
Official Name
Mohd Annuar , Mohd Isa
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Isa, M. A.M.
Isa, Mohd Annuar
Isa, Mohd Annuar Mohd
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Scopus Author ID
57194941166
Researcher ID
HPU-9865-2023

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Performance evaluation of Edge-based segmentation methods for electrical tree image analysis in high-voltage experiments

2025-06 , Mohd Annuar Mohd Isa , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Baharuddin Ismail , Mohamad Firdaus Azahari , Mohamad Kamarol Jamil , Noor Syazwani Mansor , Abdullahi A. Mas’ud , Firdaus Muhammad-Sukki

This research evaluates the performance of edge-based segmentation methods in analysing two-dimensional (2D) electrical tree images obtained during high-voltage (HV) electrical tree experiments. Non-uniform illumination in 2D optical images poses challenges in accurately extracting and measuring the original treeing image. Edge segmentation emerges as a promising solution to precisely distinguish tree structures from the insulation background within the image. Cross-linked polyethylene (XLPE) samples were subjected to HV stress for real-time propagation observation, followed by extraction and segmentation of treeing images using edge-based operators. The findings emphasize the superiority of the Roberts edge operator in accurately detecting electrical trees, showcasing the highest average accuracy of 97.01% and 99.58% specificity, while also demonstrating relatively high sensitivity. Moreover, the Roberts method provide much precisely measures the propagation length and width than conventional measurement method, closely approximating the actual tree measurements. This research emphasizes the significance of accurate segmentation for investigating electrical tree propagation in XLPE materials and provides recommendations for future research, especially in HV XLPE cable manufacturing.

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Investigation of Scribing Quality Defect of Thin Film Solar Cell Using Machine Vision

2020-12-18 , Shuaimi R. , Rizalafande Che Ismail , Hazwan Hadzir M.N. , Mohd Annuar Mohd Isa , Asral Bahari Jambek

Laser micromachining provide significant effect in thin film solar industrial field especially in determining cell efficiency of each panels. However, there is an issue in determining scribing failure or defect on solar module. This research aims to investigate the defects of laser micromachining process in thin film solar module in manufacturing fields. Machine vision inspection system is used as inspection tools and to investigate the defect of laser micromachining in thin film solar cells. As a result, two major defects is define which is scribe line quality and scribe line position defects in every scribe line. By identifying the defect cause by laser micromachining through machine vision, quality control plan can be taken together to prevent reoccurrence.

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Review of Edge-based Image Segmentation on Electrical Tree Classification in Cross-linked Polyethylene (XLPE) Insulation

2021-06-11 , Mohd Annuar Mohd Isa , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Afifah Shuhada Rosmi , Isa M. , Wan Azani Wan Mustafa , Kamarol M. , Mansor N.S. , Nazar N.S.M.

Electrical trees are the degradation events most linked with partial discharge (PD) activity in cross-linked polyethylene (XLPE) insulation of high voltage (HV) cables. To investigate tree structures and forms, study of electrical tree structures for morphological analysis often carried out using optical microscopy. However, since the noise induced by the occlusion and illumination, as well as by non-uniform intensity either from optical device's setting or the non-standard readout procedure, causes the deterioration of the original microscopy images, resulting in critically loses of information pertaining the tree structures making it difficult to obtained accurate measurement. Image segmentation is one of the potential solutions as it is well-suited for extracting information or certain features from microscopy images. This paper provides review of several segmentation techniques applied on the classification of electrical tree image acquired through lab environment. The works can be separated into three stages. The first step includes the preparation of samples and collection of treeing images by means of real-time microscopy observations. The captured data would then be pre-processed to achieve image binarization. In the next step, image segmentation process is conducted using existing edge-based segmentation methods including Prewitt, Roberts, Canny and Kirsch's templates. Later, comparative analysis will be performed using IQA (image quality assessment) metrics of accuracy, sensitivity, specificity, false-positive-rate and the Matthews Correlation Coefficient (MCC) as the final step. Performance-wise indicates that Kirsch's template able to segment most of the treeing pixels with accuracy of 97.63%, 63% sensitivity, 98.18% specificity and 0.46 MCC while showing low pixels misclassification. This result provides better justification for the integration of the edge-based technique in developing image segmentation algorithm well-matched for the electrical tree analysis in the future.

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Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

2023-01-01 , Mohd Annuar Mohd Isa , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Jamil M.K. , Muzamir Isa , Afifah Shuhada Rosmi , Mohd Aminudin Jamlos , Wan Azani Wan Mustafa , Nurulbariah Idris , Mas’ud A.A.

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.

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Review Study of Image De-Noising on Digital Image Processing and Applications

2023-03-01 , Abdulah C.S.K. , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Mohd Annuar Mohd Isa , Afifah Shuhada Rosmi , Wan Azani Wan Mustafa , Ahmad Zaidi Abdullah , Wan Nor Munirah Ariffin , Jamil M.K.M.

This paper reviews several studies of image de-noising on digital image processing and applications. Noisy images contain different noise that exist either due to environment or electronic interferences. Ergo, de-noising is crucial to eliminate the noise that disturb data collecting process. The impact of de-noising on image processing can result for accurate and precise data collected from the image. Additionally, de-noising process required several crucial steps that help to enhance knowledge on digital image and its application. Hence, study and understanding de-noising can improve multiple aspect such as image quality, data sensitivity and specificity, accuracy of the collected data, and increase the percentage of each parameter.

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