Warning Sign Analysis Of Traffic Sign Data-Set Using Super Vised Spiking Neuron Technique
In this paper, two types of conditions have been applied to analyze the performance of SNN towards usable traffic sign, which are hidden region and rotational effect. There are 20 warning traffic signs being focused on where there are regularly seen around Malacca area. These traffic sign needed to...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Science Publishing Corporation
2018
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25342/2/IJET-16898.pdf http://eprints.utem.edu.my/id/eprint/25342/ https://www.sciencepubco.com/index.php/ijet/article/view/16898/7306 |
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| Summary: | In this paper, two types of conditions have been applied to analyze the performance of SNN towards usable traffic sign, which are hidden region and rotational effect. There are 20 warning traffic signs being focused on where there are regularly seen around Malacca area. These traffic sign needed to be embedded in this system as a databased to counter the output for mean error and recognition process for both conditions applied. Early hypothesis was design as the mean error and recognition process will degraded its performance as more intrusion get introduced in the system. For hidden region, the values show a critically rising error value at 62.5% = 0.123. While 0%. For recognition process at 6.25% hidden region, 100% of images are correctly matchup to its own image. At 50% of hidden ages are perfectly recognized to its own image. At 60%, there is 30% of image able to recognize leaving others at 70%, 80% and 90% degrees rotation of images were outperformed. In view of element occasion driven handling, they open up new skylines for creating models with a colossal sum limit of recollecting and a solid capacity to quick adjustment. SNNs include another component, the transient hub, to the representation limit and the handling capacities of neural systems. |
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