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All Journal JOIV : International Journal on Informatics Visualization Emerging Science Journal
Abdul Rahman, Abdul Aziz
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Computer Science & IT Environmental Science

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Deep Learning-based Utility Pole Safety Assessment from Visual Data Abbas M. Elsayed, Mohamed; Hashim, Noramiza; Abdul Rahman, Abdul Aziz; Alhayek, Mohamed
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3039

Abstract

Utility poles are crucial infrastructure components, and efficiently assessing the safety of these structures and ensuring they adhere to the clearance guidelines, which specify the minimum distance between the pole and any surrounding objects, remains a challenge; the current manual inspection process is time-consuming, costly, and often subjective. This work proposes an automated deep learning-inspired solution to improve utility pole detection and measure the clearance distance. The biggest challenge was the lack of a comprehensive pole dataset; therefore, we collected a dataset containing utility poles in varied backgrounds, environments, and conditions. We compared data augmentation techniques and employed them to address the limited dataset size. The proposed approach consists of two main stages: pole detection and differentiation and pole distance measurement. The first stage is a comparison of multiple object detection models on our utility pole dataset; we used the results from the best-performing model to estimate the distance between the two pole objects. The results show that our pipeline with the YOLOv8 model outperforms SSD and achieves 83% accuracy in classifying pole compliance. The system can accurately detect and estimate clearance violations even with limited data. The success of the pipeline opens opportunities for future research; obtaining depth by using additional sensors or deep learning models could enhance the detection module. Scaling the approach to large utility pole networks while retaining real-time performance could lead to improved autonomous infrastructure maintenance.
Impact of the COVID-19 Pandemic on Audit Quality: Lessons and Opportunities Hazaea, Saddam A.; Tabash, Mosab I.; Abdul Rahman, Abdul Aziz; Khatib, Saleh F. A.; Zhu, Jinyu; Chong, H Gin
Emerging Science Journal Vol. 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-06

Abstract

This study aims to identify the impact of COVID-19 on audit quality based on the investigation of three auditing aspects, namely: audit fees, audit procedures, and auditors' salaries in Saudi Arabia and Yemen. For data collection, fifty-five (55) questionnaires were distributed to internal auditors, external auditors, managers of audit offices, and financial managers. Eleven managers of audit offices and auditors were interviewed. A descriptive, regression analysis, and T-test were used. The study results reveal that the audit quality has been significantly affected due to the devastating effect of COVID-19 on audit fees, audit procedures, and audit staff salaries. In addition, the results show that Yemen is severely affected due to several factors, which include a lack of modern auditing systems. Also, private ownership of establishments and the absence of laws for determining audit fees negatively impacted the audit quality. Being the first of a practical kind, this study provides a significant contribution to the existing literature on the impact of COVID-19 on the quality of auditing. This would be useful for corporations, audit offices, auditors, and researchers. Moreover, this study can bridge the identified research gap on this topic and provide empirical evidence about the impact of COVID-19 on audit quality. Doi: 10.28991/esj-2022-SPER-06 Full Text: PDF
Co-Authors Abbas M. Elsayed, Mohamed Alhayek, Mohamed Chong, H Gin Hashim, Noramiza Hazaea, Saddam A. Khatib, Saleh F. A. Tabash, Mosab I. Zhu, Jinyu