Model Training Services

We specialize in training deep learning models for plastic pollution identification and analysis.

An underwater scene with various fish swimming near a plastic bag and other debris, set against a clear blue water background. The upper section of the image captures a reflection of the surface environment.
An underwater scene with various fish swimming near a plastic bag and other debris, set against a clear blue water background. The upper section of the image captures a reflection of the surface environment.
Data Collection Stage

Collecting and preprocessing ocean images and data from various sources for analysis.

Model Construction

Building and training deep learning models to optimize plastic pollution detection capabilities.

System Integration

Integrating trained models with decision algorithms for effective ocean cleanup solutions.

woman wearing yellow long-sleeved dress under white clouds and blue sky during daytime

Theresearchdesignwillbedividedintofourmainstages.Thefirststageisdata

collectionandpreprocessing.WewilluseAPIstoobtainoceanimagesandrelateddata

frommultiplesourcessuchassatelliteimages,UAVaerialphotography,andmarine

monitoringbuoys.Then,wewillcleanandannotatethesedatatobuildahigh-quality

plasticpollutiondataset.Thesecondstageismodelconstructionandtraining.Based

onOpenAI'sAPI,wewillselectappropriatedeeplearningmodels,suchasconvolutional

neuralnetworks(CNNs),andtrainthemforthetaskofplasticpollutionidentification,

optimizingthemodelperformancebycontinuouslyadjustingparameters.Thethirdstage

issystemintegrationandtesting.

testing.Wewillintegratethetrainedmodelwiththecleanup

decisionalgorithmintoasystem,andusesimulateddataanddatafromactualpilot

seaareastotestthesystemandevaluateitsvariousperformanceindicators.Thefourth

stageispracticalapplicationandlong-termmonitoring.Wewilldeploythissystem

inselectedmarineareasforlong-termplasticpollutionmonitoringandcleanup

decisionsupport,andregularlycollectdataontheactualcleanupeffect,compareand

analyzethesystemoutputresultswiththeactualsituation,andcontinuouslyimprove

thesystem.