Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison

Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison

Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned...

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Bibliographic Details
Main Author: Sievert, Rolf
Format: Others
Language:English
Published: Linköpings universitet, Datorseende 2021
Subjects:
Machine learning
Multiclass
Deep learning
Instance segmentation
Object segmentation
Iterative stratification
Mask R-CNN
DetectoRS
Imbalanced dataset
Classification
Detection
Segmentation
Litter
Trash
TACO
COCO
MMDetection
Multinomial
Cybercom
AI
Artificial intelligence
Land-based litter
Computer vision
Maskininlärning
Djupinlärning
Instanssegmentering
Objektsegmentering
Obalanserat dataset
Klassificering
Detektion
Segmentering
Skräp
Artificiell intelligens
Datorseende
Computer Vision and Robotics (Autonomous Systems)
Datorseende och robotik (autonoma system)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173

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