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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tadas, Shubham | - |
| dc.date.accessioned | 2022-08-06T10:36:19Z | - |
| dc.date.available | 2022-08-06T10:36:19Z | - |
| dc.date.issued | 2022-05-05 | - |
| dc.identifier.uri | http://192.168.3.232:8080/jspui/handle/123456789/3396 | - |
| dc.description.abstract | Traffic sign detection and recognition are vital in the improvement of clever vehicles. Detection and recognition of traffic signs have for quite some time been at the focal point of interest for significantly affecting the wellbeing of the driver. Programmed street signs recognition is turning into a piece of Driver Assisting Systems whose job is to increment wellbeing and driving solace. Considering different datasets of various RTO/Traffic Signs, the location module will distinguish the particular sign and show Alerts for drivers. Traffic sign recognition is generally founded on the shape and variety credits of traffic signs, and traffic sign recognition is frequently utilized with classifiers, for example, convolutional neural network (CNNs). The response time of the relative multitude of above tasks will be determined and contrasted with demonstrate that the CNN executes quicker (25ms/outline). | en_US |
| dc.subject | Algorithms | en_US |
| dc.subject | Conventional neural networks | en_US |
| dc.subject | Database | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Recognition System | en_US |
| dc.title | RTO SIGN RECOGNITION FOR DRIVER ALERT | en_US |
| Appears in Collections: | Computer | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| PID 12 IJARCCE.2022.115111.pdf | 520.35 kB | Unknown | View/Open |
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