Marine debris detection using a multi-feature pyramid network

Gupta, Swamita; Khurana, Yash; Atrey, Jonathan; Gupta, Swagata; Krishnamoorthy, Parthiban

doi:10.1080/2150704x.2023.2183480

Cited by 5 publications

(1 citation statement)

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“…Their RF classifier works well mainly with images taken in spring and summer. Gupta et al (2023) present a novel approach named multi-feature pyramid network (MFPN), which consists of three subnetworks: feature extractor, feature pyramid, and pooling block. These subnetworks are concatenated to form an end-to-end network and are evaluated on the MARIDA dataset by achieving a pixel accuracy of 84%.…”

Section: Index References Expressionmentioning

confidence: 99%

On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring

Karakuş

2023

Front. Remote Sens.

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Marine plastic pollution is an emerging environmental problem since it pollutes the ocean, air and food whilst endangering the ocean wildlife via the ingestion and entanglements. During the last decade, an enormous effort has been spent on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides informative earth observation products, and the current technology offers further essential development. Despite the advances in the last decade, there is still a way to go for marine plastic monitoring research where challenges are rarely highlighted. This paper contributes to the literature with a critical review and aims to highlight literature milestones in marine debris and suspected plastics (MD&SP) monitoring by promoting the computational imaging methodology behind these approaches along with detailed discussions on challenges and potential future research directions.

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Section: Index References Expressionmentioning

confidence: 99%