Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images

Rijal, Nabin; Gutta, Ravi Teja; Cao, Tingting; Lin, Che‐Jen; Bo, Qirong; Zhang, Jing

doi:10.1109/icivc.2018.8492790

Cited by 56 publications

(25 citation statements)

References 5 publications

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“…We apply the double-channel convolutional neural network and weighted feature fusion to the classification and regression tasks, complete the tasks of air quality grade measurement and air quality index measurement. 4. Through experiments, we prove the effectiveness of the proposed method, and demonstrate the influence of different weights and different network structure on system performance.…”

mentioning

confidence: 79%

“…The extracted features are analyzed and calculated to get air quality measurement values. The imagebased deep learning methods [2,3,4,5] The main contributions of this paper:…”

Section: Introductionmentioning

confidence: 99%

“…In the study of air quality measurement related to deep learning, Chao Zhang [2] built a convolutional neural network, improved the convolutional layer activation function and classification layer activation function, proposed an improved activation function of convolutional neural network EPAPL, and used a Negative Log-Log Ordinal Classifier to replace softmax Classifier in the classification layer, used the environment image to train their network model for classification prediction, completed measurement task of 2.5 and 10 in six grades; Avijoy Chakma et al [3] used convolutional neural network training images for feature extraction, combined with random forest classification, and classified the air quality shown in the images into three grades of good, middle, and bad. Nabin Rijal [4] adopts a method of neural network ensemble learning, they used three different convolutional neural networks, VGG16, InceptionV3 and Resnet50 to respectively conduct regression training on image 2.5 values. The predicted values of the input 2.5 of the three networks were input as feature into a feedforward network for training to predict the image 2.5 values.…”

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confidence: 99%

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Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

et al. 2019

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Environmental air quality affects people's life, obtaining real-time and accurate environmental air quality has a profound guiding significance for the development of social activities. At present, environmental air quality measurement mainly adopts the method that setting air quality detector at specific monitoring points in cities and timing sampling analysis, which is easy to be restricted by time and space factors. Some air quality measurement algorithms related to deep learning mostly adopt a single convolutional neural network to train the whole image, which will ignore the difference of different parts of the image. In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images. Our method mainly includes two aspects: ensemble learning of double-channel convolutional neural network and selflearning weighted feature fusion. We constructed a double-channel convolutional neural network, used each channel to train different parts of the environment images for feature extraction. We propose a feature weight self-learning method, which weights and concatenates the extracted feature vectors, and uses the fused feature vectors to measure air quality. Our method can be applied to the two tasks of air quality grade measurement and air quality index (AQI) measurement. Moreover, we build an environmental image dataset of random time and location condition. The experiments show that our method can achieve nearly 82% accuracy and a small mean absolute error (MAE) on our test dataset. At the same time, through comparative experiment, we proved that our proposed method gained considerable improvement in performance compared with single channel convolutional neural network air quality measurements.

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confidence: 79%

“…The extracted features are analyzed and calculated to get air quality measurement values. The imagebased deep learning methods [2,3,4,5] The main contributions of this paper:…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

et al. 2019

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“…In contrast to conventional image classification tasks, the classification of haze images aims to estimate the severity levels of the visual haze, instead of distinguishing between different objects. Existing studies usually look into extracting effective haze features [2,16,22,28]. However, it has been observed that even the state-of-the-art convolutional neural networks (CNNs) [16,27,30] encounter issues in distinguishing the haze classes in light haze settings, caused by the diversity of background scenes.…”

Section: Introductionmentioning

confidence: 99%

“…The handcrafted-feature-based classifiers [24,29,31] usually focus on extracting effective visual features and use the classifiers such as SVM to classify the haze classes. Such data-driven approach alleviates introducing manual biases, but it introduces the need of feature engineering with high computational cost for feature extraction [2,22,28]. Finally, the deep learning algorithms use CNNs [32,33] with network ensembles [22,27], multi-branch training [17,28], and pre-training [2,5], to classify haze images in an end-to-end manner, which typically achieve much better performance than the threshold-based and the handcrafted-feature-based methods.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Contrastive Masking for Visual Haze Classification

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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Haze classification has gained much attention recently as a costeffective solution for air quality monitoring. Different from conventional image classification tasks, it requires the classifier to capture the haze patterns of different severity degrees. Existing efforts typically focus on the extraction of effective haze features, such as the dark channel and deep features. However, it is observed that the light-haze images are often mis-classified due to the presence of diverse background scenes. To address this issue, this paper presents an unsupervised contrastive masking (UCM) algorithm to segment the haze regions without any supervision, and develops a dual-channel model-agnostic framework, termed magnifier neural network (MagNet), to effectively use the segmented haze regions to enhance the learning of haze features by conventional deep learning models. Specifically, MagNet employs the haze regions to provide the pixel-and feature-level visual information via three strategies, including Input Augmentation, Network Constraint, and Feature Enhancement, which work as a soft-attention regularizer to alleviates the trade-off between capturing the global scene information and the local information in the haze regions. Experiments were conducted on two datasets in terms of performance comparison, parameter estimation, ablation studies, and case studies, and the results verified that UCM can accurately and rapidly segment the haze regions, and the proposed three strategies of MagNet consistently improve the performance of the state-of-the-art deep learning backbones. CCS CONCEPTS• Computing methodologies → Classification and regression trees.

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Advances in Air Quality Monitoring: A Comprehensive Review of Algorithms for Imaging and Sensing Technologies

Elbestar,

Aly,

Ghannam

2024

Advanced Sensor Research

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Air pollution is a major global concern, leading to serious health problems and environmental damage. This article provides a comprehensive review of historical and current methods used to monitor and predict air quality. It emphasizes the ongoing need for better monitoring techniques. 47 studies are critically analyzed, and computational advancements in air quality monitoring are categorized into sensor‐based and image‐based techniques. This review reveals that sensor‐based algorithmic methods, representing 62% of the reviewed literature, are reliable but often lack flexibility and real‐time monitoring capabilities. On the other hand, image‐based techniques, while innovative, are limited by the size and diversity of datasets, primarily functioning only during daylight hours. To address these limitations, a hybrid approach that integrates both sensor and image‐based methods is proposed. This aims to enhance monitoring by allowing users to visualize pollution levels through an augmented reality layer. The proposed model seeks to provide mobile users with the ability to accurately monitor surrounding air quality by establishing a comprehensive image‐based dataset that includes various features not previously considered in existing datasets.

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Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images

Cited by 56 publications

References 5 publications

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Unsupervised Contrastive Masking for Visual Haze Classification

Advances in Air Quality Monitoring: A Comprehensive Review of Algorithms for Imaging and Sensing Technologies

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