Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach

Niu, Bowen; Feng, Quanlong; Yang, Jianyu; Chen, Boan; Gao, Bingbo; Liu, Jiantao; Yi, Li; Gong, Jianhua

doi:10.1080/10106049.2022.2164361

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…For developing the models, the datasets were divided into two groups, where 80% of the data used for model training and 20% (Niu et al, 2022) for testing the original data without augmentation. On the other hand, when augmentation was adopted, the dataset was splitted into 80% for model training, 10% for validation and 10% for testing (Marin et al., 2021).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Intelligent solid waste classification system using combination of image processing and machine learning models

Qdais

Shatnawi

AL-Alamie

2023

Preprint

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Solid waste is a major issue in all countries around the world. Solid waste classification and segregation prior to reuse, recycling or recovery is an important step toward sustainable waste management. Traditional manual sorting of solid waste is a labour intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques are widely applied. This study is aiming to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open (Trashnet) and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Intelligent solid waste classification system using combination of image processing and machine learning models

Qdais

Shatnawi

AL-Alamie

2023

Preprint

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“…Niu et al [16] modelled an innovative DL technique for solid waste mapping in higher resolution images. By incorporating a Swin-Transformer and a multi-scale dilated CNN, both global and local features have been aggregated.…”

Section: Literature Reviewmentioning

confidence: 99%

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Ashary,

Jambi,

Ashari

et al. 2023

CSSE

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Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing environmental pollution. Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data and metrics. Therefore, this study designs an Entropy-based Feature Fusion using Deep Learning for Waste Object Detection and Classification (EFFDL-WODC) algorithm. The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects. In the presented EFFDL-WODC system, two major procedures can be contained, such as waste object detection and waste object classification. For object detection, the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network. In addition, entropy feature fusion-based models such as VGG-16, SqueezeNet, and NASNet models are used. Finally, the EFFDL-WODC technique uses a graph convolutional network (GCN) model performed for the classification of detected waste objects. The performance validation of the EFFDL-WODC approach was validated on the benchmark database. The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

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“…Shi et al [29] proposed a waste classification method based on a multi-layer hybrid convolutional neural network, changing the number of network modules and channels to improve the model's performance. Niu et al [33] proposed a weakly supervised learning method that utilizes multi-scale dilated convolutional neural networks and Swin-Transformer to aggregate local and global features for rough extraction of solid waste boundaries. However, these methods cannot obtain the actual distribution edges of solid waste piles to achieve pixel-level classification, and are generally limited to small-scale areas or single scenarios [22,34,35].…”

Section: Introductionmentioning

confidence: 99%

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

Liu,

Zhao,

Zhang

et al. 2024

Applied Sciences

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The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

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Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach

Cited by 7 publications

References 27 publications

Intelligent solid waste classification system using combination of image processing and machine learning models

Intelligent solid waste classification system using combination of image processing and machine learning models

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

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