Mohamed Elshaikh Elobaid Said Ahmed
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Mohamed Elshaikh Elobaid Said Ahmed
Official Name
Mohamed Elshaikh Elobaid , Said Ahmed
Alternative Name
Ahmed, Mohamed Elshaikh Elobaid Said
Said Ahmed, Mohamed Elshaikh Elobaid
Elobaid, Mohamed Elshaikh
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Scopus Author ID
57190012447
Researcher ID
R-7502-2019

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A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images

2021-08-27 , Hasimah Ali , Ahmad Firdaus Ahmad Zaidi , Wan Khairunizam Wan Ahmad , Mohd Shuhanaz Zanar Azalan , Tengku Amran T.S. , Ahmad M.R. , Mohamed Elshaikh Elobaid Said Ahmed

Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.

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Recognition of different utility pipes size of ground penetrating radar images at different penetration depth

2024-02-08 , Nasri M.I.S. , Hasimah Ali , Ahmad Firdaus Ahmad Zaidi , Shazmin Aniza Abdul Shukor , Ahmad M.R. , Amran T.S.T. , Siti Marhainis Othman , Siti Nurul Aqmariah Mohd Kanafiah , Mohamed Elshaikh Elobaid Said Ahmed

Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.

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Hyperbola detection of ground penetrating radar using deep learning

2024-02-08 , Zahir N.H.M. , Hasimah Ali , Nasri M.I.S. , Masuan N.A. , Ahmad Firdaus Ahmad Zaidi , Mohd Shuhanaz Zanar Azalan , Amin M.S.M. , Ahmad M.R. , Mohamed Elshaikh Elobaid Said Ahmed

Ground Penetrating Radar (GPR) is a geophysical method using high resolution electromagnetic used to acquire the information of underground. The electromagnetic (EM) waves produces from the antenna consisting of transmitter and receiver. The waves from the transmitter penetrates into the ground and reflect backs to the surface that receive by the antenna receiver. The antenna can lie within the range of 10MHz to 1000MHz to determine the shallow or deep penetration. Higher value of antenna will result in shallow penetration and otherwise for lower antenna. The process of recognition of buried objects is challenging task especially in the construction area to ensure safety and the quality of civil building. The GPR will display the mapping image on its control unit screen. If there are objects underground have detected, the image will display the hyperbola shape to indicate the target of the object. A vast number of data makes it difficult to classify each and every one of it either the image data is in which classes or categories. If there are many hyperbola present in image also makes it difficult to locate the accurate position. Due to this, deep learning technique by means of ResNet50 has been used in this research for hyperbola recognition in GPR image. A series of experiments has been conducted on the GPR dataset collected at Agency Nuclear Malaysia. Based on the results obtained, the deep learning model successfully learn the image feature. The accuracy of the model classified for this GPR data using ResNet50 gives 90% accuracy. Therefore, the proposed method for image recognition shows the promising results with all the GPR images are correctly recognize. Further, region of interest of hyperbola signature has been represented by a rectangular box indicates the hyperbola location

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