Inspection and Quality Checking of Ceramic Cup Using Machine Vision Technique: Design and Analysis

Ali, Nursabillilah Mohd; Karis, Mohd Safirin; Tarusan, Siti Azura Ahmad; Wong, Gao-Jie; Aras, Mohd Shahrieel Mohd; Bahar, Mohd Bazli; Abidin, Amar Faiz Zainal

doi:10.11113/jt.v79.11280

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be said that this project can help the driver to detect Cautionary traffic sign without any disturbance. At the same time, it will give a benefit to all users and can decrease the road accident [24][25][26][27][28].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Karis¹,

Ali²,

Ghafor³

et al. 2018

IJET

Self Cite

View full text Add to dashboard Cite

In this paper, 19 cautionary traffic signs were selected as a database and 3 types of conditions have been proposed. The conditions are 5 different time of image taken; hidden region and anticlockwise rotation are all the experiments design that will shows all the errors in producing the it’s mean value and the performance of traffic sign recognition. Initial hypothesis was made as the error will become larger as the interruption getting bigger. Based on the results of the five-different time of image taken, the error gives the best performance; less error when time is between 8am to 12am due to the brightness factors and the sign can be recognize clearly during noon session. The hidden region conditions show good performances of the detection and recognition of the system depend on the lesser coverage of the hidden region introduce on traffic sign because if the hidden region coverage is huge the database will get confuse and take a longer time to do the recognition process. Lastly, in anticlockwise rotation shows that 90o gave large value of error causing the system unable to recognize sign perfectly rather than 135o angle. To sum-up, detection and recognition process are not depending on higher number of angle but the process solely depending on their value of sample for each traffic signs. The error will give the impact towards traffic sign recognition and detection process. In conclusion, SNN can perform the detection and recognition process to all objects as in the future the system will become more stable with the right technique on spiking models and well-developed technology in this field.

show abstract

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Karis¹,

Ali²,

Ghafor³

et al. 2018

IJET

Self Cite

View full text Add to dashboard Cite

show abstract

“…This type of attitude can easily lead to an accident that gives injury or kills innocent people. Previously, many works focused on intelligent application for image processing research [8][9][10][11][12]. As purposed, SNN will give fast response to assist road user to detect incoming traffic sign.…”

Section: Introductionmentioning

confidence: 99%

Guide Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Ali¹,

Karis²,

Ali³

et al. 2018

IJET

Self Cite

View full text Add to dashboard Cite

In this paper, 20 guided traffic signs mostly displayed around Malacca area were selected as project databased. Early hypothesis was made as the error for each usable image will increased as more interference introduced to the original image used. Three types of conditions which are hidden region, image brightness and image rotation were selected as an experiment to analyze the performance of each sign used. Each condition will perform a specific error to generate their mean value and in the same, image recognition will take place in the matchup process. By focusing on the result, it produces hidden region critically ascending mean error value at 62.5% = 0.07 and has average value at others points. For image brightness effect, it shows a higher mean error value collected at less brightness points and non-stable pattern at 10% to 60% brightness. As for rotation upshot, the values show a critically ascending for error value at 22.5% and slightly increase at 2% to 5% rotation point. For the recognition process, at 6.25% hidden region, almost 70% of images are correctly matched to its own classes while at 62.5% hidden region only 40% of images are correctly matched to its own classes and leaving 2 images to outperform. For -40% brightness, 45% of images are correctly matched to its own classes while at 60% brightness 65% of images are correctly matched to its own classes and leaving 1 image to outperform. Lastly, at 2.5 degree rotation, 85% of images are correctly matched to its own classes while at 25° rotation, 45% of images are correctly matched to its own classes and leaving 2 images to outperform. Finally, the error forms will affect the final output response of the detected traffic signs used.

show abstract

Inspection and Quality Checking of Ceramic Cup Using Machine Vision Technique: Design and Analysis

Cited by 2 publications

References 12 publications

Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Guide Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Contact Info

Product

Resources

About