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020 _aTHE0010020 (Local)
_qHardback
040 _aUMPSA
_beng
_cUMPSA
_erda
090 _aPSM .C66 2023 r Bc.
100 0 _aConnie Lee Wai Yan,
_eauthor.
245 1 0 _aSpeed bumps and potholes detection using yolov4 /
_cConnie Lee Wai Yan
264 1 _aKuantan, Pahang :
_bUMPSA,
_c2023
264 4 _c© 2023
300 _axiv, 81 pages :
_billustrations ;
_e1 CD-ROM
336 _2rdacontent
_atext
337 _2rdamedia
_aunmediated
338 _2rdacarrier
_avolume
347 _2rda
_atext file
_bPDF
500 _aCentre for Mathematical Sciences
502 _aBachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023
504 _aIncludes bibliographical references
520 3 _aRoad maintenance is a challenging task in Malaysia. Malaysia has seen an increase in the number of fatal transport accidents, such as road accidents caused by potholes, year after year. Aside from potholes, speed bumps increase the number of injuries in road traffic accidents, even if no fatalities occur. Rather of waiting for the government to remedy the problem, there is an increasing need for a low-cost automatic detection of speed bumps and potholes. Many recent techniques have demonstrated promising results when using deep learning to various object identification tasks. Convolutional Neural Networks (CNNs) able to learn how to extract significant characteristics from an image. Many recent techniques have demonstrated promising results when using deep learning to various object identification tasks. In this paper, two image datasets has been download from Kaggle. The datasets are annotated by LabelImg and trained YOLO (You Only Look Once) version 4. The result is evaluated based on the confusion matrix, precision, recall, f1-score and mAP. The model is tested on different speed bumps and pothole images, and it detects with a reasonable accuracy. The expected outcome is to build a model that can effectively detect speed bumps and potholes to help drivers and car manufacturers.
610 2 0 _aCentre for Mathematical Sciences
_xDissertations
650 0 _aUniversities and colleges
_xDissertations
650 0 _aFinal Year Project
_xDissertations
942 _2lcc
_cPSM