Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development
In the context of sustainable urban development, effective garbage management plays a crucial role. However, traditional methods encounter limitations in terms of data quality and quantity. The research on automatic garbage image recognition and classification methods based on deep learning has been...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824007993 |
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| _version_ | 1846160399369502720 |
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| author | Yan Zhou Zhaoqi Wang Shirong Zheng Li Zhou Lu Dai Hao Luo Zecheng Zhang Mingxiu Sui |
| author_facet | Yan Zhou Zhaoqi Wang Shirong Zheng Li Zhou Lu Dai Hao Luo Zecheng Zhang Mingxiu Sui |
| author_sort | Yan Zhou |
| collection | DOAJ |
| description | In the context of sustainable urban development, effective garbage management plays a crucial role. However, traditional methods encounter limitations in terms of data quality and quantity. The research on automatic garbage image recognition and classification methods based on deep learning has been gaining attention. This study proposes an integrated garbage image recognition and classification method that combines ResNet-50, YOLOv5, and weakly supervised CNN algorithms. The aim is to enhance both the accuracy and efficiency of image recognition, optimize intelligent garbage management, and promote urban sustainable development planning. The ResNet-50 model is employed to extract meaningful features from images and train weakly supervised CNN models for subsequent training and prediction. This enables the analysis of urban environmental development trends and the formulation of planning measures. Through evaluation on four representative public datasets, the proposed method outperforms several traditional algorithms in terms of accuracy, efficiency, and stability in garbage image recognition systems. Notably, on the HGI-30 dataset, the algorithm achieves significant improvements by reducing inference time by over 48.6%, FLOPs by over 46.5%, and MAPE by over 41%. These enhancements greatly enhance the accuracy and robustness of garbage image classification, highlighting the substantial significance of this method in the realms of garbage management and environmental protection. |
| format | Article |
| id | doaj-art-5df84907c7dc435fba805f3fce959c7a |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-5df84907c7dc435fba805f3fce959c7a2024-11-22T07:36:11ZengElsevierAlexandria Engineering Journal1110-01682024-12-01108415427Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban developmentYan Zhou0Zhaoqi Wang1Shirong Zheng2Li Zhou3Lu Dai4Hao Luo5Zecheng Zhang6Mingxiu Sui7Department of Mathematics, Northeastern University, 95131, CA, USA; Corresponding author.Core data platform, Cardlytics corporation, 95035, CA, USASoftware engineering, Indeed corporation, 98008, WA, USADepartment of Management in Analytics, McGill University, 27708, Montréal, CanadaDepartment of Civil and Environmental Engineering, University of California, Berkeley, 94025, CA, USARecommendation Group, Match Group corporation, 94539, CA, USADepartment of Computer Science, New York University, 10012, NY, USADepartment of Applied Mathematics and Computational Science, University of Iowa, 08648, NJ, USAIn the context of sustainable urban development, effective garbage management plays a crucial role. However, traditional methods encounter limitations in terms of data quality and quantity. The research on automatic garbage image recognition and classification methods based on deep learning has been gaining attention. This study proposes an integrated garbage image recognition and classification method that combines ResNet-50, YOLOv5, and weakly supervised CNN algorithms. The aim is to enhance both the accuracy and efficiency of image recognition, optimize intelligent garbage management, and promote urban sustainable development planning. The ResNet-50 model is employed to extract meaningful features from images and train weakly supervised CNN models for subsequent training and prediction. This enables the analysis of urban environmental development trends and the formulation of planning measures. Through evaluation on four representative public datasets, the proposed method outperforms several traditional algorithms in terms of accuracy, efficiency, and stability in garbage image recognition systems. Notably, on the HGI-30 dataset, the algorithm achieves significant improvements by reducing inference time by over 48.6%, FLOPs by over 46.5%, and MAPE by over 41%. These enhancements greatly enhance the accuracy and robustness of garbage image classification, highlighting the substantial significance of this method in the realms of garbage management and environmental protection.http://www.sciencedirect.com/science/article/pii/S1110016824007993Garbage image recognitionResNet-50YOLOv5Internet of Things (IoT)Sustainable urban development planning |
| spellingShingle | Yan Zhou Zhaoqi Wang Shirong Zheng Li Zhou Lu Dai Hao Luo Zecheng Zhang Mingxiu Sui Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development Alexandria Engineering Journal Garbage image recognition ResNet-50 YOLOv5 Internet of Things (IoT) Sustainable urban development planning |
| title | Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development |
| title_full | Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development |
| title_fullStr | Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development |
| title_full_unstemmed | Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development |
| title_short | Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development |
| title_sort | optimization of automated garbage recognition model based on resnet 50 and weakly supervised cnn for sustainable urban development |
| topic | Garbage image recognition ResNet-50 YOLOv5 Internet of Things (IoT) Sustainable urban development planning |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824007993 |
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