Damaged road detection using Image Processing and Deep Learning

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.

Xehetasun bibliografikoak
Egile Nagusiak:	Swadesh, Shimran Mahbub, Ahmed, Rifat, Hossain, MD. Imran, Rahman, MD. Raihan
Beste egile batzuk:	Rodoshi, Ahnaf
Formatua:	Thesis
Hizkuntza:	en_US
Argitaratua:	Brac University 2022
Gaiak:	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)
Sarrera elektronikoa:	http://hdl.handle.net/10361/17596

Antzeko izenburuak

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Classification of peripheral blood cell images using deep learning
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Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
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An android application to predict human activity using a deep learning LSTM model
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DeepWPD: A deep learning based wavelet packet decomposition to remove Moiré pattern from screen capture images.
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Comprehensive analysis and development of deep learning models for Bengali character’s spectrogram image classification in child speech: introduction of spectro SETNet
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Predicting regional accents of Bengali language using deep learning
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Classification of Bangla regional languages and recognition of artificial Bangla speech using deep learning
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Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
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Document template identification and data extraction using machine learning and deep learning approach
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An implementation and analysis of deep learning models for the detection of wheat diseases
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Pothole detection using lightweight network models
nork: Abdullah, S. M., et al.
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Facial expression recognition: convolutional attentional masking network and ensemble approach
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Sentiment analysis on Bangladesh airlines review data using machine learning
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An interpretable transformer based approach to classify Malaria from blood cell images
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Image processing and deep learning approach to evaluate earthquake resistance of urban buildings
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Skin cancer detection and classification using multiple optimized deep convolutional neural network
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Detecting Deepfake images using deep convolutional neural network
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Deep learning-based waste classification system for efficient waste management
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