Classification of imbalanced municipal solid waste image datasets
- 1College of Business Information Technology, Hunan Vocational College of Commerce
Abstract
To address model overfitting and long-tail omissions caused by the shortage of minority class samples in municipal solid waste image collection, this study proposed a classification algorithm integrating an Improved Generative Adversarial Network, a Multi-Scale Attention Network, and a Dynamic Focal Loss. The proposed method employed an Improved Generative Adversarial Network to alleviate data imbalance by expanding minority class images, a Multi-Scale Attention Network to capture multi-scale spatial semantics, and a Dynamic Focal Loss to adaptively adjust gradient weights for difficult samples. Experimental results reveal an accuracy of 95.88%, a recall of 96.14%, and a misclassification ratio for minority categories of less than 0.03%. For simulated experiments, the inference latency is reduced to 14.8 ms, throughput achieved is 67.5 FPS, and the memory requirement is approximately 1.8 GB. Based on these findings, it can be concluded that the proposed approach demonstrates great anti-confusion and lightweight nature in extremely imbalanced data distribution scenarios. This study provided a reliable solution for industrial-grade intelligent waste sorting.
Related
- Harnessing fuzzy forest intelligence: A novel mechanism modeling approach for particle matter concentration in sewage sludge–municipal solid waste co-incineration
- Coupled negative-ion and HHO assistance stabilizes combustion and suppresses emissions in small-scale municipal solid waste incineration
- Integrated Intensification and Nutrient Recovery Strategies in Two-Stage Anaerobic Co-Digestion of Sewage Sludge and the Organic Fraction of Municipal Solid Waste: State of the Art, Engineering Challenges of Scale-Up, and Perspectives
- Circular economy for municipal solid waste: A systematic review on resource management, recycling, and recovery
- Municipal Solid Waste Incineration with Energy Recovery: A Critical Review of Process Performance, Emissions, Residues, and System Integration
BibTeX
@article{luo2026classification,
title = {{Classification of imbalanced municipal solid waste image datasets}},
author = {Xiaofen Luo},
journal = {Discover Environment},
year = {2026},
month = oct,
doi = {10.1007/s44274-026-01086-4},
url = {https://doi.org/10.1007/s44274-026-01086-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - Classification of imbalanced municipal solid waste image datasets
AU - Xiaofen Luo
PY - 2026
DA - 2026/10/25
JO - Discover Environment
DO - 10.1007/s44274-026-01086-4
UR - https://doi.org/10.1007/s44274-026-01086-4
AB - To address model overfitting and long-tail omissions caused by the shortage of minority class samples in municipal solid waste image collection, this study proposed a classification algorithm integrating an Improved Generative Adversarial Network, a Multi-Scale Attention Network, and a Dynamic Focal Loss. The proposed method employed an Improved Generative Adversarial Network to alleviate data imbalance by expanding minority class images, a Multi-Scale Attention Network to capture multi-scale spatial semantics, and a Dynamic Focal Loss to adaptively adjust gradient weights for difficult samples. Experimental results reveal an accuracy of 95.88%, a recall of 96.14%, and a misclassification ratio for minority categories of less than 0.03%. For simulated experiments, the inference latency is reduced to 14.8 ms, throughput achieved is 67.5 FPS, and the memory requirement is approximately 1.8 GB. Based on these findings, it can be concluded that the proposed approach demonstrates great anti-confusion and lightweight nature in extremely imbalanced data distribution scenarios. This study provided a reliable solution for industrial-grade intelligent waste sorting.
SN - 2731-9431
ER -