Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Xu, Linquan; Xu, Linji; Chen, Yuying; Zhang, Yuantao; Yang, Jixiang

doi:10.1021/acsestwater.1c00466

Cited by 19 publications

(8 citation statements)

References 23 publications

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“…In view of the above issues, propose an algae small object detection model MAgic (Morphable Attention Based Algal Tiny Object Detection Model) as shown in Figure 1. 33 First, we modify the loss function which is sensitive to the size of the target, which alleviates the tendency of the model to fit the larger size target when the parameters are updated, and reduces the possibility of ignoring the small target of algae as the background noise in the learning process. At the same time, we introduce an advanced attention mechanism to obtain the head and improve the sampling rate of small targets.…”

Section: Model Proposalmentioning

confidence: 99%

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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Section: Model Proposalmentioning

confidence: 99%

Detection of algal tiny objects based on morphological features

Yuan,

Peng,

Shi

et al. 2024

Concurrency and Computation

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“…Based on the expanded dataset of 16 algal families, ResNeXt was modified and a classification accuracy of 0.9997 was finally achieved. Xu et al [ 14 ] expanded 13 algal species through data enhancement, forming a relatively balanced dataset among different algal species. Based on this dataset, they designed a new CNN algorithm, which obtained the lowest classification probability of 0.939.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the acquisition and collection of algal images are difficult due to their obvious regional nature. Most algal researchers use data augmentation methods to expand the numbers of algal images [ 11 , 12 , 13 , 14 ]. These extended algal datasets can easily enable classification algorithms to achieve accuracy of more than 99% [ 11 , 13 , 15 ] and average precision of more than 80% [ 15 , 16 ], which leads to the classification and detection performance of CNN on algal dataset not being able to be well mined.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Self-Organized Detection System for Algae

Gong

Sun

et al. 2023

Sensors

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Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.

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“…Building on the successful implementation of the CNN in analyzing images, – we applied the CNN to the analysis of semidilute solution viscosity. Figure outlines the workflow of the CNN implementation in determining the polymer/solvent-specific parameters { B g , B th , P e } from the solution viscosity represented as a surface in the 3D space of ( cl 3 , N w , η sp ) (step 1).…”

Section: Cnn Application To Analysis Of Solution Viscositymentioning

confidence: 99%

Deep Learning for Determination of Properties of Semidilute Polymer Solutions

Sayko,

Jacobs,

Dominijanni

et al. 2023

ACS Appl. Polym. Mater.

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The ability to predict polymer solution viscosity is essential for polymer characterization and processing. Here, we use a synergistic approach combining the scaling theory of polymer solutions and convolutional neural network (CNN) models to obtain system-specific parameters and describe semidilute solution viscosity in the unentangled and entangled solution regimes. The scaling approach relies on the existence of a characteristic microscopic length scale�the solution correlation length (correlation blob size) ξ�which uniquely defines macroscopic solution properties. It is based on a relationship between the solution correlation length ξ = lg ν /B and the number of monomers g per correlation volume for polymers with the monomer projection length l. The system-specific set of parameters B g , B th , and 1 in the corresponding solution regime with the scaling exponent ν = 0.588, 0.5, and 1, respectively. Applying two CNN models, we obtained the sets B g and B th from the solution specific viscosity, η sp , as a function of concentration, c, and weight-average degree of polymerization, N w . The CNN was trained on theoretically generated datasets converted to sparse images representing the normalized specific viscosity η sp /N w (cl 3 ) 1/(3ν−1) in the unentangled Rouse regime. The trained CNN was utilized in automated data analysis of the solution viscosity of polystyrene, poly(ethylene oxide), poly(methyl methacrylate), poly(acrylonitrile-co-itaconic acid), cellulose, sodium hyaluronate, hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, cellulose tris(phenyl carbamate), xanthan gum, galactomannan, and sodium κ-carrageenan in water, organic solvents, and ionic liquids. This approach produced values of the B-parameters with mean absolute percentage differences of less than 6% from the corresponding values determined by the manual data analysis. The B-parameters are then used to obtain the packing number P e defining the onset of entanglements in polymer solutions and to describe semidilute solution viscosity as a function of concentration and N w .

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Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Cited by 19 publications

References 23 publications

Detection of algal tiny objects based on morphological features

Detection of algal tiny objects based on morphological features

An Efficient Self-Organized Detection System for Algae

Deep Learning for Determination of Properties of Semidilute Polymer Solutions

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