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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | REVIEW ON TRAFFIC SIGN DETECTION AND RECOGNITION USING DEEP LEARNING UNDER CHALLENGING WEATHER CONDITIONS |
| Authors Name: | Stephen Mascarenhas , Vaibhav Kolpe , Pratik Shinde , Ankita Tapase , Dr. Aparna Pande |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_195275
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| Published Paper Id: | IJNRD2305397 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | http://doi.one/10.1729/Journal.34242 |
| Abstract: | Traffic sign detection and recognition is an emerging area of research that is gaining popularity with the release of Deep Learning and Computer Vision in modern automobiles. With changing weather conditions and environmental hazards, it is difficult for modern automobiles to identify and recognize road signs. TSDR problem in different CCs (Haze, Snow, Dirty Lens, Lens Blur and Rain) is studied in this paper, with an emphasis on the resulting performance degradation, & suggest a prior enhancement focused TSDR architecture built on Convolutional Neural Networks (CNN). The 4 modules which give most accurate results in detecting and recognizing traffic signs (Challenge Classifier, Enhancement Block, Sign Localizer and Sign Classifier) are flexible, also modifiable on the basis of actual weather conditions. The CURE-TSD dataset, which consists of traffic recordings shot using various CCs, is used to assess the effectiveness of mentioned methods. On the CURE-TSD Dataset, experimental results show that VGG-16 achieves a total accuracy of 99.98%. |
| Keywords: | Deep learning, Convolutional Neural Network, Challenging Conditions, Traffic Sign Detection, Traffic Sign Recognition, Enhancement Block, Challenge Classifier. |
| Cite Article: | "REVIEW ON TRAFFIC SIGN DETECTION AND RECOGNITION USING DEEP LEARNING UNDER CHALLENGING WEATHER CONDITIONS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d755-d760, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305397.pdf |
| Downloads: | 000118753 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305397 Registration ID: 195275 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.34242 Page No: d755-d760 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305397 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305397 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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