A Biological Sensor System Using Computer Vision for Water Quality Monitoring

Yuan, Fei; Huang, Yifan; Chen, Xin; Cheng, En

doi:10.1109/access.2018.2876336

Cited by 41 publications

(16 citation statements)

References 13 publications

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“…Computer Vision and Neural Networking approaches are also integrated to monitor water quality. As water quality classification models, the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural network techniques are used [ 9 ]. In [ 10 ], machine learning was aligned with IoT devices to sense and analyze water quality factors.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

Dhruba

Alam

Khan

et al. 2022

Journal of Healthcare Engineering

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The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth.

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Section: Introductionmentioning

confidence: 99%

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

Dhruba

Alam

Khan

et al. 2022

Journal of Healthcare Engineering

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“…A few of those studies attempted to classify and detect plant leaf diseases, but all of them reported that CNN models also learn structures in the background with features similar to those of leaf lesions, resulting in poor performance in lesion recognition [27]- [29]. In addition, a study on a crop pest recognition method in a natural scene [30] and another on the development of a fish behavior-based biological monitoring system [31] found that complex backgrounds affect the accuracy of target object recognition. Another study [32], in which an automatic classification system for plankton images was built, mentioned that background noise interference caused by a marine environment and non-ideal imaging conditions might affect the efficacy of a CNN model.…”

Section: Related Workmentioning

confidence: 99%

A Morphological Image Preprocessing Method Based on the Geometrical Shape of Lesions to Improve the Lesion Recognition Performance of Convolutional Neural Networks

Kimori

2022

IEEE Access

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Convolutional neural networks (CNNs) play an important role in computer vision-related tasks for medical imaging. However, the quality of raw images in the dataset can be insufficient for the CNN model to learn the features of the target object. When the input image contains a complex background, the CNN model focuses on regions that are not essential for lesion recognition, such as background structures, leading to less accurate output prediction. This paper shows that this problem can be efficiently solved by an image preprocessing method based on mathematical morphology, which uses a priori knowledge about the lesion shape. The proposed method consists of h-dome transformation based on the geometrical shape information of the lesion region, and subsequent image histogram-modification processes, and has the ability to selectively enhance the lesion region from the background. This allows for the creation of images that explicitly represent the important region to be learned by the CNN model. Experiments on pulmonary nodule classification in chest x-ray images and skin lesion-region segmentation in dermatoscopic images demonstrate that the CNN models trained on the preprocessed dataset created by the proposed method achieve remarkable performance improvements compared to the CNN models trained on the original dataset.INDEX TERMS Medical images, convolutional neural networks, mathematical morphology, h-dome transform.

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“…The rapid development of underwater observation technology provides underwater optical vision with very broad application prospects. As a typical application of underwater optical vision, underwater visual target detection plays an increasingly important role in underwater security [1][2][3][4], marine exploration [5,6], fish farming [7] and marine ecology [8,9]. Therefore, the achievement of underwater autonomous operation through visual target detection completion by use of underwater optical images has become a research hotspot in the field of computer vision [1].…”

Section: Introductionmentioning

confidence: 99%

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

Zhang

Fang

Pan

et al. 2021

Sensors

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Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.

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A Biological Sensor System Using Computer Vision for Water Quality Monitoring

Cited by 41 publications

References 13 publications

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

A Morphological Image Preprocessing Method Based on the Geometrical Shape of Lesions to Improve the Lesion Recognition Performance of Convolutional Neural Networks

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

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