Identification and enumeration of cyanobacteria species using a deep neural network

Baek, Sang-Soo; Pyo, Juncheol; Pachepsky, Yakov; Park, Yongeun; Ligaray, Mayzonee; Ahn, Chi‐Yong; Kim, Young-Hyo; Chun, Jong Ahn

doi:10.1016/j.ecolind.2020.106395

Cited by 57 publications

(44 citation statements)

References 28 publications

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“…For energy conservation and pollution reduction, the solution of blast furnace slag smelting problem is more inclined to use the theoretical model to predict the melting behavior in the blast furnace slag. Convolutional neural network (CNN) [31]- [34], region convolutional neural network (R-CNN) [35]- [37], fast R-CNN and support vector machine (SVM) [38], [39] have become popular in the field of target tracking and positioning [40], [41]. CNN and other methods have been used to extract the depth features and adaptively fuse them [42].…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

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This paper focuses on how to perspective the melting behavior of solid iron tailings in molten blast furnace slag and take a new non-contact visual analytical method to predict its melting law. The optimized convolution neural network (CNN) is used to track the moving target in charge coupled device (CCD) camera system efficiently and accurately, and the melting behavior of SiO 2 is described by coordinate translation transformation theory. Hierarchical agglomerative clustering (HAC) and delaunay triangulation were used to extract the characteristic parameters of the melting process of SiO 2. The prediction model of the melting rate of SiO 2 at high temperature was established by least square fitting (LSF) and dimensional analysis, and compared with the actual melting rate of SiO 2 obtained by experiments. The results show that the melting characteristics of SiO 2 at high temperature are in accord with certain function rule. The performance of optimized CNN in terms of processing time and accuracy is significantly improved, and the fusion rate prediction model of SiO 2 is verified by 100% accuracy. It provides theoretical support and model basis for the improvement of slag cotton preparation technology. INDEX TERMS Melting rate, target tracking, feature extraction, dimensional analysis, best match.

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

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“…Finally, we have not included a comparison with previous approaches in this work. The reason is that the most related works [ 26 , 27 , 28 ] do not approach the imaging protocol in a similar way to ours, and thus they are not comparable. Specifically, PlanktoVision [ 26 ] and PLASA [ 27 ] use fluorescence images along with multiple-focus images obtained using computerized microscopes.…”

Section: Resultsmentioning

confidence: 81%

“…Additionally, PLASA also used several magnifications in the analysis. On the other hand, in in [ 28 ] the focus and magnification is manually adjusted for each of the imaged species. Thus, in this sense, unlike previous approaches, our method allows to detect single-specimen phytoplankton images from single-focus and magnification images that are systematically obtained using conventional a microscope.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, it would be desirable to provide automatic analysis solutions that can take advantage of these conventional microscope images, so that an automatic analysis solution, aiming at releasing the expert taxonomists from tedious identification and counting works, can be made available at low cost. However, to the best of our knowledge, these images have not been the target of automated analyses, with the exception of some few related works [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…While this eases the detection and segmentation of phytoplankton specimens, it involves a complicated imaging protocol, which probably requires the use of computerised microscopes [ 26 , 27 ]. In other works [ 28 ], the proposed imaging method can not even be performed in a systematic way, as they require an human expert to select the appropriate focal point and magnification used for each of the imaged species. Conversely, this work is focused on the analysis of conventional microscope images that are acquired using a systematic imaging protocol aiming at minimising the required human intervention.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

Rivas-Villar

Rouco

Penedo

et al. 2020

Sensors

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Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

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Detection of algal tiny objects based on morphological features

Yuan,

Peng,

Shi

et al. 2024

Concurrency and Computation

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SummaryThe dense and toxic blooms formed by cyanobacteria in aquatic environments pose significant threats to public health and aquatic ecosystems. Timely monitoring and prevention of cyanobacterial blooms in freshwater bodies are thus imperative. Although object detection methods have been applied in the field of algae identification, existing research faces several challenges. A primary issue is the overly idealistic setting of training sets, which are disconnected from the actual water quality environments, impeding practical algae identification and water quality monitoring. In this paper, we collect 2024 microscopic images of algae from a reservoir in Southern China, forming a comprehensive and diverse dataset for object detection. Addressing the aforementioned challenges, we propose an attention‐based strategy for the detection of tiny algal objects, which effectively samples and extracts features from algal targets. Our model uniquely leverages the morphable characteristics of algae, enhancing the accuracy and efficiency of identification. The training and improvement results of this model presented in our study are expected to aid in establishing a future system for real‐time algae monitoring and water quality assessment.

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Identification and enumeration of cyanobacteria species using a deep neural network

Cited by 57 publications

References 28 publications

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

Detection of algal tiny objects based on morphological features

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