Automatic Number Plate Recognition (ANPR) with YOLOv3-CNN

2022-11-07 · Rajdeep Adak, Abhishek Kumbhar, Rajas Pathare, Sagar Gowda

We present a YOLOv3-CNN pipeline for detecting vehicles, segregation of number plates, and local storage of final recognized characters. Vehicle identification is performed under various image correction schemes to determine the effect of environmental factors (angle of perception, luminosity, motion-blurring, and multi-line custom font etc.). A YOLOv3 object detection model was trained to identify vehicles from a dataset of traffic images. A second YOLOv3 layer was trained to identify number plates from vehicle images. Based upon correction schemes, individual characters were segregated and verified against real-time data to calculate accuracy of this approach. While characters under direct view were recognized accurately, some numberplates affected by environmental factors had reduced levels of accuracy. We summarize the results under various environmental factors against real-time data and produce an overall accuracy of the pipeline model.

📄 PDF Abstract BibTeX arXiv:2211.05229

Code (0)

등록된 구현이 없습니다.

Tasks

object-detection Object Detection

Methods 이 논문이 사용한 방법론

Average Pooling 설명 없음

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…

Batch Normalization 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Residual Connection 설명 없음

BNB Customer Service Number +1-833-534-1729 설명 없음

Exploration of an End-to-End Automatic Number-plate Recognition neural network for Indian datasets

2022-07-14 · Sai Sirisha Nadiminti, Pranav Kant Gaur, Abhilash Bhardwaj

Indian vehicle number plates have wide variety in terms of size, font, script and shape. Development of Automatic Number Plate Recognition (ANPR) solutions is therefore challenging, necessitating a diverse dataset to ser…

Chaurah: A Smart Raspberry Pi based Parking System

2023-12-28 · Soumya Ranjan Choudhaury, Aditya Narendra, Ashutosh Mishra, Ipsit Misra

The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to …

Optical Character Recognition Optical Character Recognition (OCR)Raspberry Pi 3

Automatic Number Plate Recognition using Random Forest Classifier

2023-03-26 · Zuhaib Akhtar, Rashid Ali

Automatic Number Plate Recognition System (ANPRS) is a mass surveillance embedded system that recognizes the number plate of the vehicle. This system is generally used for traffic management applications. It should be ve…

Management Segmentation

Character Recognition of Nepali Number Plate

2026-06-27 · Satyasa Khadka, Sandhya Baral, Sudip Tiwari, Sharad Kumar Ghimire arxiv

This paper presents a robust Automatic Number Plate Recognition (ANPR) system tailored for Nepali license plates written in Devanagari script. In this paper, a pipelined model was used that integrates YOLO-based models f…

Data Augmentation

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

2022-02-11 · Muhammad Usama, Hafeez Anwar, Abbas Anwar, Saeed Anwar

We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image varia…

License Plate Detection License Plate Recognition Vocal Bursts Type Prediction