Earticle

초록

영어

The vehicle-related challenges are escalating, such as vehicle thefts, traffic violations, and outdated license registrations. Computer visions using artificial intelligence can be utilized as a tool to find solutions. However inaccuracies in license plate recognition can occur due to environmental factors such as camera dust, camera location, and vibration causing blurry images and distracting. To solve these challenges, we propose an automatic license plate recognition system. Proposed system operates in two main stages: license plate detection and recognition. Initially, it detects and isolates the license plate from the vehicle image. Following this, the proposed system determines whether the plate is single or double-lined and proceeds to accurately classify and arrange the characters on the plate in their correct order. Utilizing the YOLOv7 object detection model and trained on the Karplate dataset, in addition the model is training on a data augmentation that represent the environmental factors, where we notes that the YOLOv7 _training is more accurate in recognizing the car plates and accurately recognizes various license plate layouts, presenting a significant results in vehicle identification technology.

목차

Abstract
1. Introduction
2. Related works
3. Methods
3.1. Dataset
3.2. License plate detection and recognition
3.3. Experiment setup
4. Experiment result
5. Conclusions
References

키워드

저자

• Alabdulwahab Abrar [ School of Computing Gachon University ]
• Sang-Woong Lee [ School of Computing Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004