Abstract:
Efficient waste management is an essential component of sustainable development,
because using traditional methods can be time-consuming and hazardous to the
environment. Automating trash categorization procedures through the incorporation of
deep learning algorithms is a possible solution to these problems. This article suggests a
garbage detection system that successfully classifies different waste kinds by using deep
learning algorithms. Through the application of machine learning and deep learning, the
system can discern between various waste categories, making appropriate sorting and
disposal easier. The waste categorization efficacy of DenseNet architecture and transfer
learning. The study uses pre-trained algorithms to improve sorting accuracy and refine
them using a dedicated waste dataset. We investigate the possibility of DenseNet's dense
connection patterns to extract complex characteristics and relevant patterns from waste
photos. Significant accuracy gains are shown by the experimental findings, demonstrating
the effectiveness of this strategy in waste classification system optimization. The
experimental findings show significant accuracy gains, demonstrating this strategy's
effectiveness in optimizing waste classification systems procedures. Through the creation
of more precise and automated waste categorization systems, this research helps to
promote environmental sustainability through the advancement of robust waste sorting
technology. ResNet152, Inceptionv3, DenseNet were used in this work. The best
accuracy comes from ResNet152, which is 98%
