ArticlePloS one2023
Application of MobileNetV2 to waste classification.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- A Systematic Review of AI-Based Techniques for Automated Waste Classification.Sensors (Basel, Switzerland) · 2025Pooled it
- EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.BMC medical informatics and decision making · 2026Article
- A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization.Scientific reports · 2026Article
- Deep residual and hybrid CNN models for confidence-aware real-world waste classification for sustainable waste management.Scientific reports · 2026Article
- Compact waste image classification with multi-student CNNs and edge-oriented model selection.Frontiers in artificial intelligence · 2026Article
- Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics.Analytical chemistry · 2025Article
- Towards sustainable solutions: Effective waste classification framework via enhanced deep convolutional neural networks.PloS one · 2025Article
- An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today, the topic of waste separation has been raised for a long time, and some waste separation devices have been installed in large communities. However, the vast majority of domestic waste is still not properly sorted and put out, and the disposal of domestic waste still relies mostly on manual classification. The research in this paper applies deep learning to this persistent problem, which has important significance and impact. The domestic waste is classified into four categories: recyclable waste, kitchen waste, hazardous waste and other waste. The garbage classification model trained based on MobileNetV2 deep neural network can classify domestic garbage quickly and accurately, which can save a lot of labor, material and time costs. The absolute accuracy of the trained network model is 82.92%. In comparison with CNN network model, the classification accuracy of MobileNetV2 model is 15.42% higher than that of CNN model. In addition, the trained model is light enough to be better applied to mobile.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.