Angoth Lakshmn

• Author(s):

  ANGOTH LAKSHMAN | M. Madhu Vinay | P. Kalyani | V. Deepthi | M.Upendra

• Keywords:

  YOLO (You Look Only Once), OCR, CNN, HoG, Haar Features

• Abstract:

  Every Country Has Seen An Increase In Motorcycle Accidents Over The Years Due To Social And Economic Differences As Well As Regional Variations In Transportation Circumstances. One Common Mode Of Transportation For Those In The Middle Class Is A Motorbike. One Of The Leading Causes Of Road Accidents That Result In Fatalities Nowadays. Among Them, Motorcycling Accidents Are Common And Can Cause Severe Injuries. A Motorcycle Rider's Helmet Is One Of The Most Important Components Of Safety. But Many People Choose To Ignore The Recommendation To Wear A Helmet. In Current Situation, We Come Across Various Problems In Traffic Regulations In India Which Can Be Solved With Different Ideas. Riding Motorcycle/mopeds Without Wearing Helmet Is A Traffic Violation Which Has Resulted In Increase In Number Of Accidents And Deaths In India. Existing System Monitors The Traffic Violations Primarily Through CCTV Recordings, Where The Traffic Police Have To Look Into The Frame Where The Traffic Violation Is Happening, Zoom Into The License Plate In Case Rider Is Not Wearing Helmet. But This Requires Lot Of Manpower And Time As The Traffic Violations Frequently And The Number Of People Using Motorcycles Is Increasing Day-by-day. What If There Is A System, Which Would Automatically Look For Traffic Violation Of Not Wearing Helmet While Riding Motorcycle/moped And If So, Would Automatically Extract The Vehicle’s License Plate Number. Recent Research Have Successfully Done This Work Based On CNN, R-CNN, LBP, HoG, HaaR Features, Etc. But These Works Are Limited With Respect To Efficiency, Accuracy Or The Speed With Which Object Detection And Classification Is Done. In This Research Work, A Non-Helmet Rider Detection System Is Built Which Attempts To Satisfy The Automation Of Detecting The Traffic Violation Of Not Wearing Helmet And Extracting The Vehicles' License Plate Number. The Main Principle Involved Is Object Detection Using Deep Learning At Three Levels. The Objects Detected Are Person, Motorcycle/moped At First Level Using YOLOv2, Helmet At Second Level Using YOLOv3, License Plate At The Last Level UsingYOLOv2. Then The License Plate Registration Number Is Extracted Using OCR (Optical Character Recognition). All These Techniques Are Subjected To Predefined Conditions And Constraints, Especially The License Plate Number Extraction Part. Since, This Work Takes Video As Its Input, The Speed Of Execution Is Crucial. We Have Used Above Said Methodologies To Build A Holistic System For Both Helmet Detection And License Plate Number.

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Other Details

• Paper id:

  IJSARTV11I6103841

• Published in:

  Volume: 11 Issue: 6 June 2025

• Publication Date:

  2025-06-30

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