Analysis of deep learning models on low-light pest detection

Analysis of deep learning models on low-light pest detection

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

Bibliografiske detaljer
Main Authors:	Irtiza, Md. Samin, Ahmed, Fattah, Haque, Md. Tahmidul, Tamim, Arifur Rahman, Sultana, Samia
Andre forfattere:	Chakrabarty, Amitabha
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Low-light images Enhancement network Deep learning Object detection Insect Machine learning Computer vision
Online adgang:	http://hdl.handle.net/10361/21848

Lignende værker

Classifying insect pests from image data using deep learning
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A modern technique to detect potholes by Computer Vision and Deep Learning
af: Saif, Muntasir Mahmud, et al.
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Insect infection and immunity

Literature review of insect pests and diseases of rice in Bangladesh.
Udgivet: (1977)

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Udgivet: (2022)

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Udgivet: (2023)

The nature of light : what is a photon? /
Udgivet: (2008)

Friends of the rice farmer : helpful insects, spiders, and pathogens /
af: Shepard, Barclay M.
Udgivet: (1987)

Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
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Udgivet: (2024)

Designing with light /
af: Meyers, Victoria
Udgivet: (2006)

Fundamentals of architectural lighting /
af: Mills, Samuel M.
Udgivet: (2018)

Pest detection system using machine learning techniques
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Udgivet: (2022)

Design of an electricity efficient street light system
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Udgivet: (2023)

Normalizing images in various weather and lighting conditions using Pix2Pix GAN
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Udgivet: (2024)

Occluded object detection for autonomous vehicles employing YOLOv5, YOLOX and Faster R-CNN
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Udgivet: (2022)

Image recognition by deep learning
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Udgivet: (2018)

Dragonflies and damselflies

ভারতের কীটপতঙ্গ /
af: মানি, এম. এস

Colour & light : spatial experience /
Udgivet: (2017)

KDANet: optical recognition for Bangla language using deep neural networks
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Udgivet: (2023)

An interpretable transformer based approach to classify Malaria from blood cell images
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Udgivet: (2023)

Lightbook : the practice of lighting design /
af: Brandi, Ulrike
Udgivet: (2001)

Visual object recognition using deep convolutional neural network
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Udgivet: (2017)

Image processing based human detection and social distancing measurements with monitoring via fine tuned deep learning and computer vision
af: Datta, Anurag, et al.
Udgivet: (2022)

An efficient deep learning approach to detect skin Cancer
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Computer assisted learning dictionary for rural students
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Udgivet: (2018)

Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
af: Saikat, Nayem Hossain, et al.
Udgivet: (2024)

Semi-automatic system of lumen measurement of LED lights in an embedded box
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Udgivet: (2018)

Transfer learning based industrial steel plates fault diagnosis using industrial fault signals
af: Mizan, Mubasshira, et al.
Udgivet: (2024)

Corn leaf disease detection using deep convolution neural network
af: Rabbi, Rawhatur, et al.
Udgivet: (2023)

Detecting different stages of Alzheimer’s disease from MRI images using deep learning and computer vision techniques
af: Rahman, Fardin, et al.
Udgivet: (2024)

Prostate cancer detection using deep learning neural network with transfer learning approach
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A secured federated learning system leveraging confidence score to identify retinal disease
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Udgivet: (2023)

Using deep learning algorithms to detect violent activities
af: Ammar, S.M. Rojin, et al.
Udgivet: (2019)

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

Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
af: Ghosh, Kawshik Kumar, et al.
Udgivet: (2023)

Tiny-ML based person identification in dynamic motion
af: Ahmed, Mirza Raiyan, et al.
Udgivet: (2024)

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