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7.883 Call For Paper

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Author(s):

Kirupa P | Jayachandiran R | Kishore S | Pandikumar K | Prajith K
Keywords:
Abstract:
Efficient Waste Management Is A Growing Challenge In Urban And Industrial Areas, With Improper Sorting Of Recyclable And Non-recyclable Materials Contributing To Environmental Degradation And Resource Inefficiency. Traditional Manual Sorting Methods Are Time-consuming, Error-prone, And Often Unsustainable At Scale. SMART (Scrap Material Automated Recognition And Triage) Is An AI-powered Scrap Waste Sorting System Designed To Address These Issues By Automating The Identification And Classification Of Waste Materials. Leveraging Computer Vision And Machine Learning, SMART Uses A Trained Convolutional Neural Network (CNN) Model To Accurately Detect And Sort Various Types Of Waste—such As Plastics, Metals, Glass, And Paper—based On Visual Input From Camera Sensors. The System Integrates A Raspberry Pi-controlled Conveyor Belt, Real-time Object Detection Via TensorFlow Or PyTorch, And A Robotic Arm Mechanism To Physically Sort Identified Materials Into Appropriate Bins. A User-friendly Dashboard Built With React.js And Flask Provides System Monitoring, Classification Analytics, And Environmental Impact Statistics.