Automatic waste classification using deep learning and computer vision techniques

Automatic waste classification using deep learning and computer vision techniques

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Bibliografiske detaljer
Main Authors:	Akash, MD., Shama, Umme Sabiha, Dibash, Dey, Ghosh, Ria
Andre forfattere:	Karim, Dewan Ziaul
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Custom CNN Resnet50 VGG16 MobileNetV2 InceptionV3 EfficientNetB0 Pretrained Validation Accuracy Detection Classification YOLOv4 Deep learning YOLOv4-tiny Cognitive learning theory Neural networks (Computer science) Machine learning
Online adgang:	http://hdl.handle.net/10361/21220

Lignende værker

Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI
af: Athina, Fahima Hasan, et al.
Udgivet: (2022)

A comparative study of lung cancer prediction using deep learning
af: Mugdho, Aka Mohammad, et al.
Udgivet: (2023)

Real-time crime detection using convolutional LSTM and YOLOv7
af: Sakiba, Cyrus, et al.
Udgivet: (2023)

Analyzing students’ concentration in online courses through Webcam
af: Asif, Md., et al.
Udgivet: (2024)

Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks
af: Awwal, Alvina, et al.
Udgivet: (2021)

Citrus leaf disease detection by image processing
af: Chowdhury, Mahir Faisal, et al.
Udgivet: (2024)

An efficient approach for recyclable waste detection and classification using image processing techniques
af: Chowdhury, Prabal Kumar, et al.
Udgivet: (2024)

Detection of intracranial hemorrhage on CT scan images using convolutional neural network
af: Rahman, Afridi Ibn, et al.
Udgivet: (2022)

Skin disease detection and classification using deep learning
af: Shuvon, Mehedi Hasan, et al.
Udgivet: (2022)

Prediction of glaucoma from fundus images leveraging transfer learning in deep neural network
af: Ismail, Sayem Mohammad, et al.
Udgivet: (2021)

Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning.
af: Hossain, Mohammad Sakib, et al.
Udgivet: (2023)

An efficient deep learning approach to detect retinal disease using optical coherence tomographic images
af: Khan, Farhan Sakib, et al.
Udgivet: (2022)

Cassava leaf disease classification using deep learning and convolutional neural network ensemble
af: Shahriar, Hasan, et al.
Udgivet: (2022)

A deep learning approach for pneumonia classiﬁcation from chest X-Ray images with ensemble modelling and explainable AI
af: Akhter, Nasrin
Udgivet: (2023)

Plant disease diagnosis using deep transfer learning architectures- VGG19, MobileNetV2 and Inception-V3
af: Kobra, Khadija-Tul, et al.
Udgivet: (2022)

Skin cancer classification for seven types of skin lesions
af: Rahman, Md. Tawsifur, et al.
Udgivet: (2024)

Content based image search in openstack swift
af: Ali Uday, Mir Rownak, et al.
Udgivet: (2024)

Fire and disaster detection with multimodal quadcopter By machine learning
af: Afrin, Anika, et al.
Udgivet: (2023)

Covid-19 infected lung detection using machine learning
af: Islam, Md. Muntaha, et al.
Udgivet: (2022)

Diabetic retinopathy detection and classification by using deep learning
af: Hossain, Shahriar, et al.
Udgivet: (2022)

Identifying brain abnormalities using image processing and CNN models
af: Talukder, Ismat Shehrin, et al.
Udgivet: (2021)

Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
af: Shahriar, Farhan, et al.
Udgivet: (2021)

Explainable breast cancer detection from Histopathology images using transfer learning and XAI
af: Farabi, Farhan, et al.
Udgivet: (2023)

Approaching transfer learning to identify correctly worn facial masks
af: Sadid, Tausif, et al.
Udgivet: (2021)

Deep learning-based real-time pothole detection for avoiding road accident
af: Basher, Rafsan, et al.
Udgivet: (2022)

Tomato leaf disease detection using convolutional neural network
af: Hasan, Md. Riazul, et al.
Udgivet: (2023)

Violent activity detection through surveillance camera using deep learning
af: Miah, Parvez, et al.
Udgivet: (2023)

FoodieCal: a convolutional neural network based food detection and calorie estimation system
af: Mashraf, Chowdhury Zerif, et al.
Udgivet: (2021)

Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
af: Promita, Samanta Tabassum, et al.
Udgivet: (2022)

Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models
af: Islam, Tahsin, et al.
Udgivet: (2021)

Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing
af: Tasnim, Sadia, et al.
Udgivet: (2023)

Brain tumor detection with convolutional neural network
af: Galib, Abrar Tahmid, et al.
Udgivet: (2024)

An ambient assisted living system for Alzheimer’s patients
af: Abedin, Minhajul, et al.
Udgivet: (2023)

An efficient face recognition model using multiple angular images and deep neural network architecture
af: Subah, Silma, et al.
Udgivet: (2024)

A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning
af: Sarmi, Kaniz Fatima, et al.
Udgivet: (2022)

Evaluating the effectiveness of CNN-based models for diabetic retinopathy detection
af: Niaz, H.M, et al.
Udgivet: (2024)

Tomato leaf disease detection using Resnet-50 and MobileNet Architecture
af: Tahamid, Abu
Udgivet: (2021)

Retinal Diseases Detection using Deep Learning
af: Mashfi, Shahriar, et al.
Udgivet: (2023)

Leveraging robust CNN architectures for real-time object recognition from conveyor belt
af: Moon, Nowrin Tasnim, et al.
Udgivet: (2023)

Classiﬁcation of damaged vegetation areas using convolutional neural network over satellite images
af: Haque, Samiha, et al.
Udgivet: (2021)