Quadcopter-based stagnant water identification

Prasad, Meghshyam G.; Chakraborty, Abhishek; Chalasani, Raviteja; Chandran, Sharat

doi:10.1109/ncvpripg.2015.7490049

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…(i) The proposed method adapts the concepts of SIGO, GWBP and VGG16 for feature extraction in new way to solve the complex problem of multiple-class classification of clean and polluted water images. (ii) The way [18] Wu et al [25] Zhao et al .11 The limitations of the proposed method (2020) 2:2068 | https://doi.org/10.1007/s42452-020-03882-w better than existing methods for both two and multiple class classification. (v) It is also noted from experimental results that the proposed method is robust to rotation and scaling and to some extent to noise and blur at different levels.…”

Section: Discussionmentioning

confidence: 99%

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Deep invariant texture features for water image classification

Xue

Shivakumara

et al. 2020

SN Appl. Sci.

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Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.

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Section: Discussionmentioning

confidence: 99%

“…The method uses edge features to trace contours of reflections. Prasad et al [18] proposed a method based on the use of quadcopters for stagnant water image detection. The method explores color and directional features for water image detection.…”

Section: Review Of Related Workmentioning

confidence: 99%

Deep invariant texture features for water image classification

Xue

Shivakumara

et al. 2020

SN Appl. Sci.

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“…No caso específico de detecção de focos de mosquito, destacam-se as referências [14], [15], [16]. Em [14]é proposto um sistema que recebe imagens com georreferenciamento geradas pela população, e avalia-se a qualidade das mesmas, a fim de rejeitar imagens com altos níveis de distorções ou artefatos.…”

Section: Trabalhos Relacionadosunclassified

“…Por fim,é gerado um mapa de calor, onde são apontadas as regiões com mais riscos de incidência de habitats do mosquito. Os autores de [15] utilizam uma abordagem para detecção de poças d'água em imagens obtidas por um quadricóptero com um classificador SVM treinado a partir de trechos de vídeos contendo ou não regiões comágua. Imagens termais e em níveis de cinza são utilizadas para detectarágua parada em [16].…”

Section: Trabalhos Relacionadosunclassified

Acerca da Detecção Automática de Focos do Mosquito Aedes aegypti

Passos¹,

Dias²,

Alves³

et al. 2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Todos os anos, milhares de pessoas são afetadas por doenças como dengue, chikungunya, zika e febre amarela. Todas essas doenças possuem umúnico vetor em comum, o mosquito Aedes aegypti, que se reproduz emágua limpa e parada, usualmente acumulada em recipientes como pneus, garrafas, caixas d'água etc. O uso de ferramentas inteligentes pode auxiliar o trabalho dos agentes de fiscalização dos focos de mosquito, aumentando a eficiência eárea de cobertura. Esse trabalho aborda o problema de detecção automática de focos de mosquitos baseado em técnicas de visão computacional e aprendizado de máquinas. Nesse contexto, descreve-se uma base de dados de sequências de vídeos aéreos, adquiridos com um veículo aéreo não tripulado, contendo alguns desses objetos em múltiplos cenários: diferentes localidades, altitudes e disposições dos objetos. Os vídeos são devidamente calibrados para amenizar distorções da câmera e manualmente anotados quadro a quadro, viabilizando o desenvolvimento de um detector automático de objetos de interesse.

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“…To solve this major problem of building water monitoring system, many works have been published to theoretically understand inherent meanings of water images. For example, Prasad et al [7] fused color feature and optical flow to perform water identification, which achieves high performance in accuracy. Recently, Mettes et al [8] built probability-based classifiers with spatiotemporal invariant descriptors to perform tasks of water detection.…”

Section: Introductionmentioning

confidence: 99%

Attention Neural Network for Water Image Classification under IoT Environment

Zhang

Xiao

et al. 2020

Applied Sciences

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With significant development of sensors and Internet of things (IoT), researchers nowadays can easily know what happens in water ecosystem by acquiring water images. Essentially, growing data category and size greatly contribute to solving water pollution problems. In this paper, we focus on classifying water images to sub-categories of clean and polluted water, thus promoting instant feedback of a water pollution monitoring system that utilizes IoT technology to capture water image. Due to low inter-class and high intra-class differences of captured water images, water image classification is challenging. Inspired by the ability to extract highly distinguish features of Convolutional Neural Network (CNN), we aim to construct an attention neural network for IoT captured water images classification that appropriately encodes channel-wise and multi-layer properties to accomplish feature representation enhancement. During construction, we firstly propose channel-wise attention gate structure and then utilize it to construct a hierarchical attention neural network in local and global sense. We carried out comparative experiments on an image dataset about water surface with several studies, which showed the effectiveness of the proposed attention neural network for water image classification. We applied the proposed neural network as a key part of a water image based pollution monitoring system, which helps users to monitor water pollution breaks in real-time and take instant actions to deal with pollution.

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Quadcopter-based stagnant water identification

Cited by 10 publications

References 4 publications

Deep invariant texture features for water image classification

Deep invariant texture features for water image classification

Acerca da Detecção Automática de Focos do Mosquito Aedes aegypti

Attention Neural Network for Water Image Classification under IoT Environment

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