Deep Learning Architectures for Waste Detection: A Systematic Literature Review
DOI:
https://doi.org/10.69616/mit.v2i1.213Keywords:
Deep Learning, Systematic Literature Review, Waste Classification, Waste DetectionAbstract
Increasing population growth contributes to the increasing amount of waste generated, creating problems in its management. One possible alternative is the usage of deep learning-based artificial intelligence technology, which can be applied for automated waste identification and classification. This research follows Systematic Literature Review (SLR) with the aim of evaluating the performance of various deep learning architectures in waste detection. Out of 547 articles identified, 20 were synthesized with the use of selective criteria. The synthesis of the evidence shows that architectures like YOLO, Faster R-CNN, and EfficientNet have been proven to be effective in the various datasets for waste detection. Furthermore, certain other models, such as ResNet-50B and DenseNet121, performed very well in image classification with accuracy values above 90%. In this research, several challenges were identified, including high computing power and computational magnitude. For future work, the optimization of models as well as the use of standard datasets such as TrashNet and TACO are recommended research directions that might help in developing an effective, efficient, and sustainable waste sorting system.
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