Publication:
Traffic Sign Classification for Road Safety using CNN
Traffic Sign Classification for Road Safety using CNN
| cris.author.scopus-author-id | 58995737500 | |
| cris.author.scopus-author-id | 58655509500 | |
| cris.author.scopus-author-id | 54785424700 | |
| cris.virtual.department | Universiti Malaysia Perlis | |
| cris.virtualsource.department | 00268366-977d-4a95-aa89-132c5b11e561 | |
| dc.contributor.author | Haree Krishna P. | |
| dc.contributor.author | Ravindran S. | |
| dc.contributor.author | Vikneswaran Vijean | |
| dc.date.accessioned | 2024-09-28T12:56:58Z | |
| dc.date.available | 2024-09-28T12:56:58Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | In today's life, Traffic sign identification is a significant domain of environment awareness system. This traffic sign identification is becoming a top priority for modern transportation systems as it is highly essential to maintain the road safety nowadays. While detecting the traffic signs using various target detection techniques, many real-time problems are being faced like easy omission, undesirable light, inaccurate positioning for traffic signs (during detection), disorientation, motion blur, color fade, occlusion, rain, and snow. In view of these problems that the traffic signs cannot be recognized well, many novel target detection technologies are emerging, which in-turn solves these problems. This article introduces a reliable traffic sign categorization system, with the help of OpenCV for image enhancement and a five-layered Convolution Neural Network. The significance of sophisticated traffic sign identification for preventing accidents and promoting road safety is emphasized by this research that classifies traffic signs. The proposed CNN model has proved to achieve a remarkable classification accuracy and flexibility in response to changes in sign and environment, as demonstrated by the outcomes of the experiments. The strength of the proposed model has been tested on the German Traffic Sign Dataset and the experimental results have unfolded the fact that this model has recognized German traffic signs, with a better classification accuracy of 97.3%. | |
| dc.identifier.doi | 10.1109/ESIC60604.2024.10481587 | |
| dc.identifier.isbn | [9798350349856] | |
| dc.identifier.scopus | 2-s2.0-85190955353 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14170/5479 | |
| dc.language.iso | en | |
| dc.relation.grantno | undefined | |
| dc.relation.ispartof | ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings | |
| dc.relation.ispartofseries | ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings | |
| dc.subject | Classification | Convolutional Neural Network | Cross Cultural Adaptation | Image enhancement | OpenCV | |
| dc.title | Traffic Sign Classification for Road Safety using CNN | |
| dc.type | Conference Proceeding | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 466 | |
| oaire.citation.startPage | 462 | |
| oairecerif.affiliation.orgunit | Amrita Vishwa Vidyapeetham | |
| oairecerif.affiliation.orgunit | Amrita Vishwa Vidyapeetham | |
| oairecerif.affiliation.orgunit | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.scopus-author-id | 58995737500 | |
| person.identifier.scopus-author-id | 58655509500 | |
| person.identifier.scopus-author-id | 54785424700 |