Article

Deep Learning Algorithm in Waste Classification using Real-Time Object Detection

Emil R. KaburuanMercu Buana University,Informatics Engineering Department,Jakarta Barat,IndonesiaFebryo Ponco SulistyoMercu Buana University,Informatics Engineering Department,Jakarta Barat,IndonesiaMuhammad HarisMercu Buana University,Informatics Engineering Department,Jakarta Barat,IndonesiaEliyani EliyaniMercu Buana University,Informatics Engineering Department,Jakarta Barat,IndonesiaKarshiyev Dilshod Abduraxmanovich Dilshod KarshiyevTashkent Pediatric Medical Institute,Department of Biophysics, Medical Informatics,Tashkent,Uzbekistan

2024en

ABI

Abstract

Poor waste management can adversely impact the environment, hindering its optimal performance. Implementing advanced item detection methods offers a more innovative solution to waste management challenges. This study explores the use of image processing with YOLO (You Only Look Once) for trash detection. By leveraging deep learning technology, it classifies various types of waste objects. A reliable classification system facilitates faster and more accurate waste segregation. YOLOv5s, known for its fast real-time object detection capabilities, effectively identifies and categorizes different types of waste.

Topics

Advanced Neural Network Applications Recycling and Waste Management Techniques Municipal Solid Waste Management

Identifiers

DOI: 10.1109/icot64290.2024.10936955

Citations and references

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