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

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