000			03899nam a22003017a 4500
003			KOHA
005			20231103100936.0
008			231103d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3114 _bO46 2023
100	1		_aOmnondiagbe, David Agbonjague
245	1	0	_aAUTOMATED CONCRETE CRACK DETECTION USING DEEP LEARNING TECHNIQUES / _cDAVID AGBONJAGUE OMONDIAGBE; SUPERVISOR: ASSOC. PROF. DR. TAMER TULGAR
264			_c2023
300			_axii, 116 sheets; _c31 cm. _e1 CD-ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Systems Engineering Department
504			_aIncludes bibliography (sheets 81-87)
520			_aABSTRACT Concrete is one of the most commonly used material in civil engineering and construction. It is mainly used in the construction of buildings, bridges, pavements etc. These concrete infrastructures are prone to cracks and these cracks are caused by various factors. Early detection of concrete cracks guarantees the safety of concrete infrastructures. The main focus of this work is to develop an automated deep learning model for detecting concrete cracks. This research uses the Concrete Crack Image Classification (CCIC) dataset having two classes. The dataset is balanced containing 20,000 images with cracks (positive class) and 20,000 images without cracks (negative class). In this work, the effect of converting images to grayscale and fine tuning hyper parameters (batch size and Adam LR) was investigated by performing several experiments. Converting the images to grayscale resulted in simpler models with reduced parameters due to the one channel of grayscale images as compared to the three channels of RGB images. Two different architectures of CNN models, CNN 1 and CNN 2 were built and trained on RGB and grayscale images. The experimental results revealed that there is no much significant difference in the performance of the RGB and grayscale CNN models. This means that concrete crack detection relies more on structural patterns rather than color information. Although, converting the images to grayscale allows the model to focus more on learning the crack features. The models were further tested on an external data different from the development data. In this experiment, the CNN 2 model using grayscale images (batch size of 32 and Adam learning rate of 0.001) outperformed the other models and is chosen as the proposed model. The proposed model achieved a little change decrease in the difference in performance between the development data and external data. In addition, the proposed model’s generalization ability was further validated using 10 fold cross validation method. This was done to ensure that the model is not overfitting. The proposed model achieved a mean accuracy of 99.74%, mean precision of 99.83%, mean recall of 99.65%, mean F1-score of 99.74%, mean specificity of 99.83%, and mean AUC ROC of 0.9974. These performances were further compared with seven previous concrete crack detection studies that have used the CCIC dataset. From the comparative analysis, the proposed model performed better than the solutions of six of these studies in all the performance metrics compared. Keywords: Computer Vision, Concrete Crack Detection, Convolutional Neural Network, Deep Learning
650		0	_aComputer vision _vDissertations, Academic
650		0	_aConcrete _vDissertations, Academic
650		0	_aNeural networks (Computer science) _vDissertations, Academic
650		0	_aDeep learning (Machine learning) _vDissertations, Academic
700	1		_aTulgar, Tamer _esupervisor
942			_2ddc _cTS
999			_c291640 _d291640