Recognition of road sign using convolutional neural netwrok / (Record no. 26843)

MARC details
000 -LEADER
fixed length control field 02544nam a2200325 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240117175641.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240117b2022 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency CSPC
Original cataloging agency CSPC
Language of cataloging eng
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSCS D444r 2022
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Deres, Samantha Amor P.,
Relator term author.
245 1# - TITLE STATEMENT
Title Recognition of road sign using convolutional neural netwrok /
Statement of responsibility, etc. Samantha Amor P. Deres, [and 4 others].
300 ## - PHYSICAL DESCRIPTION
Extent 104 leaves.
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Source rdacarrier
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Theses (BSCS) - Computer Science
Name of granting institution Camarines Sur Polytechnic Colleges, 2022.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references.
520 3# - SUMMARY, ETC.
Summary, etc. These days, an increasing number of tasks that require image recognition use convolutional neural networks. This research suggested utilizing a convolutional approach to implement road sign recognition and it focused on dealing with uploading an image and quickly identifying which kind of road sign is present in which class of an input image. Generally, this study aimed to develop a web application that uses a Convolutional Neural Network (CNN) algorithm to recognize road sign images. Additionally, a simple and easy prototype for recognizing and classifying road signs was created using CNN algorithms in order to achieve more accurate recognition results. After the training and testing process, the trained CNN model achieved 80% performance. The model's evaluation in continous testing and training, which shows that CNN is capable of recognizing images, shows that it is successful in predicting and recognizing road signs. This evaluation was achieved using the sklearn and keras modules of Python that provides a Python interface for CNN models. Furthermore, the proposed prototype achieved good performance for road sign recognition, which provides a better recognition performance hence, according to the experimental results on the Convolutional Neural Network and according to the generated average analysis, it is suitable for image recognition.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Munar, Caroline T.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Prades, Dave M.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Saunar, Zyndrek John Paul D.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tabuena, Brianna Carlota L.,
Relator term author
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Oñate, Joseph Jessie,
Relator term adviser
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Onesa, Rosel O.,
Relator term panelist, chair
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Fortuno, Kaela Marie N.,
Relator term panelist
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Ibo, Allan O. Jr.,
Relator term panelist
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Unpublished Materials
Suppress in OPAC No
Classification part BSCS
Item part D444r
Call number prefix UTH
Call number suffix 2022
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 01/17/2024   UTH BSCS D444r 2022 006078UM 01/17/2024 01/17/2024 Unpublished Materials