Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Applied Research and Smart Technology (ARSTech)
Ashraf, Arselan
Unknown Affiliation
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study Mohd Yusof, Najiha ‘Izzaty; Sophian, Ali; Mohd Zaki, Hasan Firdaus; Bawono, Ali Aryo; Embong, Abd Halim; Ashraf, Arselan
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6317

Abstract

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.
Machine learning-based pavement crack detection, classification, and characterization: a review Ashraf, Arselan; Sophian, Ali; Shafie, Amir Akramin; Gunawan, Teddy Surya; Ismail, Norfarah Nadia
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Image-based disease detection and classification in Indian apple plant species by using deep learning Wani, Sidrah Fayaz; Ashraf, Arselan; Sophian, Ali
Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/arstech.v3i1.1021

Abstract

Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural productivity growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing an intelligent automated technique for plant disease detection is explored in this research. Deep learning is used to create a smart system for image-based disease detection in Indian apple plant species. Specifically, this study uses a convolution neural network architecture, ResNet-34, to identify diseases in apple plants. Based on 70-30% and 80-20% dataset partition, the proposed model obtained an accuracy of 97.5% and 98.4%, respectively. The results obtained from this study illustrate the productive exploration and utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that provide cure and preventative measures for the detected diseases.   
Co-Authors Bawono, Ali Aryo Embong, Abd Halim Ismail, Norfarah Nadia Mohd Yusof, Najiha ‘Izzaty Mohd Zaki, Hasan Firdaus Shafie, Amir Akramin Sophian, Ali Teddy Surya Gunawan Wani, Sidrah Fayaz