Fish Image Classification Using Deep Convolutional Neural Network

Lan, Xiaojuan; Bai, Juyang; Li, Meng; Li, Jiajun

doi:10.1145/3419635.3419643

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Liang et al [3] 2020 Shape features Convolutional neural network 98.1% Knausgård et al [4] 2022 Generic features Convolutional neural network 87.74% Böer et al [5] 2021 Morphological features DeepLabV3 and PSPNet models 96.8% Iqbal et al [6] 2019 Generic features AlexNet model 90.48% Cui et al [7] 2020 Generic features Convolutional neural network 97.5% Zhang et al [8] 2021 Morphological features Convolutional neural network 96% Montalbo and Hernandez [9] 2019 Generic features VGG16 DCNN Model 99% Mathur and Goel [10] 2021 Generic features ResNet-50 Model 98.44% Ahmed et al [11] 2022 Statistical & color features SVM classifier 94.12% Lan et al [12] 2020 Shape and texture features Deep CNN 89% Allken et al [13] 2018 Shape features A deep learning neural network 94% Andayani et al [14] 2019 In this paper, RBFNN and SVM techniques for fish image classification were presented and evaluated against the fish shape features. The contribution of this work can be summarized as: -Extracted robust features for fish image classification.…”

Section: 6%mentioning

confidence: 99%

“…Ahmed et al [11] used machine learning-based classification model support vector machine (SVM) for fish infection and they obtained an accyracy rate of 94.12%. Inception-V3 deep learning algorithm for fish image classification is proposed in [12]. To overcome the problems due to low-quality images and small data, they used data augmentation improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Robust features extraction from shape signature for fish images classification

Ahmed

Hussein

Gali

2023

IJEECS

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Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.

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Section: 6%mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust features extraction from shape signature for fish images classification

Ahmed

Hussein

Gali

2023

IJEECS

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“…The fish detection and classification discussed in [27] mandates the selection of parameters such as color features, statistical texture features, and waveletbased texture features of the color and texture sub-images through high-resolution images to obtain optimum performance. The Inception-V3 network-based fish classification depicted in [28] provides less accuracy as the images are of low resolution. The fish classification in an underwater vehicle, as discussed in [29], depicts the usage of three optimization algorithms in the convolutional network, thereby leading to complexity in classification.…”

Section: Introductionmentioning

confidence: 99%

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Sowmmiya,

Roselyn,

Sundaravadivel

2024

Information

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Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.

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“…Such existing methodologies of sorting may not yield fruitful results, and may even spoil the fish, thereby lowering its quality. The developments of image processing techniques have aided the advancement of morphometric methods for species identification [12]. The use of an image-based system allows for the automated separation of fish species, making the process more efficient and quicker, and causing less spoilage to the fish.…”

Section: Introductionmentioning

confidence: 99%

Creation of Machine Learning-Based Fish Classification Systems Based on Morphometric and Mathematical Transform Data

Mohammad Ahmar Khan

2024

jes

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The accurate classification of fish species is crucial for various ecological, conservation, and fisheries management applications. Traditional methods relying solely on manual observation or morphological characteristics can be time-consuming and prone to errors. In recent years, advancements in machine learning (ML) techniques coupled with morphometric and mathematical transform data have shown promising results in automating and enhancing fish species classification processes. This paper presents a comprehensive review of the development and application of ML-based fish classification systems utilizing morphometric measurements and mathematical transforms. The core of this paper focuses on the integration of morphometric and mathematical transform data with ML algorithms for fish classification. In this review commonly employed ML algorithms, including but not limited to support vector machines (SVM), artificial neural networks (ANN), random forests (RF), and convolutional neural networks (CNN). Furthermore, this work discuss feature selection techniques to optimize classification performance and reduce dimensionality. These may include the incorporation of multimodal data sources, transfer learning approaches, and the development of user-friendly tools for field biologists and conservationists. Overall, this paper serves as a comprehensive guide for researchers and practitioners interested in leveraging ML techniques for accurate and efficient fish species classification.

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Fish Image Classification Using Deep Convolutional Neural Network

Cited by 7 publications

References 3 publications

Robust features extraction from shape signature for fish images classification

Robust features extraction from shape signature for fish images classification

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Creation of Machine Learning-Based Fish Classification Systems Based on Morphometric and Mathematical Transform Data

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