Emmanuel Adewale Adedokun

Work place: Department of Computer Engineering, Ahmadu Bello University, Zaria, 1045, Nigeria

E-mail: wale@abu.edu.ng

Website:

Research Interests: Computer Networks, Neural Networks

Biography

Emmanuel Adewale Adedokun is a Professor at Ahmadu Bello University Zaria. He received his Ph.D. Degree in Electrical Engineering (Network Security), M.Sc. and B. Eng in Electrical Engineering in 2014, 2009, and 1999 from Ahmadu Bello University Zaria. He is a member of several professional bodies including IEEE, NSE, CPN, NCS, FOA. His areas of Expertise are Computer Networks, Security, and Emerging Technologies. He can be contacted by email at: wale@abu.edu.ng.

Author Articles
Performance Analysis of Various Image Feature Extractor Filters for Pothole Anomaly Classification

By Risikat Folashade Adebiyi Habeeb Bello-Salau Adeiza James Onumanyi Bashir Olaniyi Sadiq Abdulfatai Dare Adekale Busayo Hadir Adebiyi Emmanuel Adewale Adedokun

DOI: https://doi.org/10.5815/ijigsp.2024.01.03, Pub. Date: 8 Feb. 2024

Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.

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