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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Zamani Md Sani
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification Zamani Md Sani; Hadhrami Abd Ghani; Rosli Besar; Azizul Azizan; Hafiza Abas
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.324 KB) | DOI: 10.11591/ijece.v8i4.pp2540-2548

Abstract

Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, ???? to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.
Advances in lane marking detection algorithms for all-weather conditions Hadhrami Ab Ghani; Rosli Besar; Zamani Md Sani; Mohd Nazeri Kamaruddin; Syabeela Syahali; Atiqullah Mohamed Daud; Aerun Martin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3365-3373

Abstract

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.
Road markers classification using binary scanning and slope contours Zamani Md Sani; Hadhrami Abd Ghani; Rosli Besar; Azizul Azizan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12759

Abstract

Road markers guide the driver while driving on the road to control the traffic for the safety of the road users. With the booming autonomous car technology, the road markers classification is important in its vision segment to navigate the autonomous car. A new method is proposed in this paper to classify five types of road markers namely dashed, single, double, solid-dashed and dashed-solid which are commonly found on the two lane single carriageway. The classification is using unique feature acquired from the binary image by scanning on each of the images to calculate the frequency of binary transition. Another feature which is the slopes between the two centroids which allow the proposed method, to perform the classification within the same video frame period. This proposed method has been observed to achieve an accuracy value of at least 93%, which is higher than the accuracy value achieved by the existing methods.
Toddler monitoring system in vehicle using single shot detector mobilenet and single shot detector-inception on Jetson Nano Kok Jia Quan; Zamani Md Sani; Tarmizi Bin Ahmad Izzuddin; Azizul Azizan; Hadhrami Abd Ghani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1534-1542

Abstract

Road vehicles are today’s primary form of transportation; the safety of children passengers must take precedence. Numerous reports of toddler death in road vehicles, include heatstroke and accidents caused by negligent parents. In this research, we report a system developed to monitor and detect a toddler's presence in a vehicle and to classify the toddler's seatbelt status. The objective of the toddler monitoring system is to monitor the child's conditions to ensure the toddler's safety. The device senses the toddler's seatbelt status and warns the driver if the child is left in the car after the vehicle is powered off. The vision-based monitoring system employs deep learning algorithms to recognize infants and seatbelts, in the interior vehicle environment. Due to its superior performance, the Nvidia Jetson Nano was selected as the computational unit. Deep learning algorithms such as faster region-based convolutional neural network (R-CNN), single shot detector (SSD)- MobileNet, and single shot detector (SSD)-Inception was utilized and compared for detection and classification. From the results, the object detection algorithms using Jetson Nano achieved 80 FPS, with up to 82.98% accuracy, making it feasible for online and real-time in-vehicle monitoring with low power requirements
Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5 Hamzah Abdulmalek Al-Haimi; Zamani Md Sani; Tarmizi Ahmad Izzudin; Hadhrami Abdul Ghani; Azizul Azizan; Samsul Ariffin Abdul Karim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1585-1592

Abstract

This project aims to develop a vision system that can detect traffic lightcounter and to recognise the numbers shown on it. The system used you onlylook once version 3 (YOLOv3) algorithm because of its robust performanceand reliability and able to be implemented in Nvidia Jetson nano kit. A totalof 2204 images consisting of numbers from 0-9 green and 0-9 red. Another80% (1764) from the images are used for training and 20% (440) are used fortesting. The results obtained from the training demonstrated Totalprecision=89%, Recall=99.2%, F1 score=70%, intersection over union(IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2%and the estimate total confidence rate for red and green are 98.4% and 99.3%respectively. The results were compared with the previous YOLOv5algorithm, and the results are substantially close to each other as the YOLOv5accuracy and recall at 97.5% and 97.5% respectively.
Co-Authors Aerun Martin Atiqullah Mohamed Daud Azizul Azizan Azizul Azizan Hadhrami Ab Ghani Hadhrami Abd Ghani Hadhrami Abd Ghani Hadhrami Abdul Ghani Hafiza Abas Hamzah Abdulmalek Al-Haimi Kok Jia Quan Mohd Nazeri Kamaruddin Rosli Besar Rosli Besar Samsul Ariffin Abdul Karim Syabeela Syahali Tarmizi Ahmad Izzudin Tarmizi Bin Ahmad Izzuddin