Damaged road detection using Image Processing and Deep Learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Manylion Llyfryddiaeth
Prif Awduron:	Swadesh, Shimran Mahbub, Ahmed, Rifat, Hossain, MD. Imran, Rahman, MD. Raihan
Awduron Eraill:	Rodoshi, Ahnaf
Fformat:	Traethawd Ymchwil
Iaith:	en_US
Cyhoeddwyd:	Brac University 2022
Pynciau:	Index Terms—Deep Learning Road Crack Detection Categorization Object Recognition Transfer Learning and RELU Deep learning (Machine learning) Image processing > Digital techniques Cognitive learning theory (Deep learning)
Mynediad Ar-lein:	http://hdl.handle.net/10361/17596

id	10361-17596
record_format	dspace
spelling	10361-175962022-11-21T21:01:44Z Damaged road detection using Image Processing and Deep Learning Swadesh, Shimran Mahbub Ahmed, Rifat Hossain, MD. Imran Rahman, MD. Raihan Rodoshi, Ahnaf Mostafa, Nafis Department of Computer Science and Engineering, Brac University Index Terms—Deep Learning Road Crack Detection Categorization Object Recognition Transfer Learning and RELU Deep learning (Machine learning) Image processing -- Digital techniques Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). Computer Science has evolved enormously in the last few decades. It has now far exceeded the Human and Computer interfaces. Its recent sights are scaling, measuring, object detection, etc. Image processing and deep learning have gone through many groundworks in the last few years. Our research paper, based on YOLO V4, LeNet-5, Retina Net, and Faster-RCNN algorithms, proves that these algorithms can detect damaged roads and analyze whether we can enhance any new ways to improve the damaged road detection in real-time. However, in a real-world scenario, it is essential to comprehend the various damages in taking the appropriate action. Thus automotive industries are looking forward to innovations that can increase the efficiency in damage categorization. Along with worldwide industrialization, road damage detection systems have become significantly important both in terms of maintenance and establishment. As the Artificial Intelligence sector is making a lot of progress, faulty road detection through Image Processing and Machine Learning has proved to be a flourishing technique. We can detect damaged roads within specific provisional categories with combinations of such technological stems. In our solution, we propose a futuristic deep learning method for object recognition with the help of four different algo rithms. More specifically, our approach uses a convolutional neural network to train our model with a large dataset solely made for the project and categorize the results into a set of damages along with its comparative analysis. Shimran Mahbub Swadesh Rifat Ahmed MD. Imran Hossain MD. Raihan Rahman B. Computer Science 2022-11-21T05:14:56Z 2022-11-21T05:14:56Z 2022 2022-01 Thesis ID: 21341029 ID: 18101710 ID: 17201093 ID: 18101169 http://hdl.handle.net/10361/17596 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 42 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Index Terms—Deep Learning Road Crack Detection Categorization Object Recognition Transfer Learning and RELU Deep learning (Machine learning) Image processing -- Digital techniques Cognitive learning theory (Deep learning)
spellingShingle	Index Terms—Deep Learning Road Crack Detection Categorization Object Recognition Transfer Learning and RELU Deep learning (Machine learning) Image processing -- Digital techniques Cognitive learning theory (Deep learning) Swadesh, Shimran Mahbub Ahmed, Rifat Hossain, MD. Imran Rahman, MD. Raihan Damaged road detection using Image Processing and Deep Learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Rodoshi, Ahnaf
author_facet	Rodoshi, Ahnaf Swadesh, Shimran Mahbub Ahmed, Rifat Hossain, MD. Imran Rahman, MD. Raihan
format	Thesis
author	Swadesh, Shimran Mahbub Ahmed, Rifat Hossain, MD. Imran Rahman, MD. Raihan
author_sort	Swadesh, Shimran Mahbub
title	Damaged road detection using Image Processing and Deep Learning
title_short	Damaged road detection using Image Processing and Deep Learning
title_full	Damaged road detection using Image Processing and Deep Learning
title_fullStr	Damaged road detection using Image Processing and Deep Learning
title_full_unstemmed	Damaged road detection using Image Processing and Deep Learning
title_sort	damaged road detection using image processing and deep learning
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17596
work_keys_str_mv	AT swadeshshimranmahbub damagedroaddetectionusingimageprocessinganddeeplearning AT ahmedrifat damagedroaddetectionusingimageprocessinganddeeplearning AT hossainmdimran damagedroaddetectionusingimageprocessinganddeeplearning AT rahmanmdraihan damagedroaddetectionusingimageprocessinganddeeplearning
_version_	1814308824013602816

Damaged road detection using Image Processing and Deep Learning

Eitemau Tebyg