Connie Lee Wai Yan, <br/><br/>
Speed bumps and potholes detection using yolov4 / 
Connie Lee Wai Yan 
 - xiv, 81 pages :  illustrations ;  1 CD-ROM 
<br/><br/> Centre for Mathematical Sciences 
<br/><br/>Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 
<br/><br/>Includes bibliographical references 
<br/><br/> Road 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. 
<br/><br/><label>ISBN: </label>THE0010020 (Local) 
<br/><br/><label>Subjects--Corporate Names: </label><br/>Centre for Mathematical Sciences--Dissertations
<br/><br/><label>Subjects--Topical Terms: </label><br/>Universities and colleges--Dissertations<br/>Final Year Project--Dissertations