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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19455
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
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Published Paper Details
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| Paper Title: | Automatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels |
| Authors Name: | Lilesh Deepak Waghmare , Deepa Kulkarni |
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| Author Reg. ID: |
IJRTI_181764
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| Published Paper Id: | IJRTI2205002 |
| Published In: | Volume 7 Issue 5, May-2022 |
| DOI: | |
| Abstract: | As the urban population rises and the number of motor vehicles increases, traffic pollution is becoming a major concern in the twenty-first century. Accidents are a major cause of traffic delays since they not only result in injuries and losses for those involved, but also in lost and squandered time for others who are stuck behind the wheel. The proposed Object Detection and Tracking Technology (ODTS) would be used and expanded to automatically identify and control irregular events on CCTVs in tunnels in conjunction with a well-known deep learning network, Faster Regional Convolution Neural (Faster R- CNN), for Object Detection and Traditional Object Tracking. It enables the detection of a moving target in real time, which is typically not possible in standard object tracking systems. The proposed method takes a time frame as input for Object Detection Bounding Box discoveries, comparing current and preceding picture bounding boxes to provide a unique ID number to each moving and detecting object. [3] A video clip is the suggested system. It enables the detection of a moving target in real time, which is typically not possible in standard object tracking systems. As a result, the computer will identify any and all injuries. More specifically, because the training data set is large, it is possible to automatically improve the ODTS capabilities without modifying the programme codes. |
| Keywords: | R-Convolutional Neural Network, Object Detection, Tunnel accident detection. |
| Cite Article: | "Automatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.5 - 13, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205002.pdf |
| Downloads: | 000205154 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2205002
Registration ID:181764
Published In: Volume 7 Issue 5, May-2022
DOI (Digital Object Identifier):
Page No: 5 - 13 Country: Pune, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2205002 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2205002 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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