Fish Species Classification Using Probabilistic Neural Network

Abstract There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven classification models like neural networks require a huge amount of labelled data, otherwise they tend to over-fit to the training data and fail on unseen test data which is not involved in training. We present a state-of-the-art computer vision method for fine-grained fish species classification based on deep learning techniques. A cross-layer pooling algorithm using a pre-trained Convolutional Neural Network as a generalized feature detector is proposed, thus avoiding the need for a large amount of training data. Classification on test data is performed by a SVM on the features computed through the proposed method, resulting in classification accuracy of 94.3% for fish species from typical underwater video imagery captured off the coast of Western Australia. This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.

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Use of Convolutional Neural Network for Fish Species Classification

Journal of Maritime & Transportation Science ◽

10.18048/2020.59.08. ◽

2020 ◽

Vol 59 (1) ◽

pp. 131-142

Author(s):

Daniel Štifanić ◽

Zlatan Car

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Fish Species ◽

Video Recording ◽

Activation Function ◽

Classification Performance ◽

Learning Rate ◽

Machine Learning Algorithms ◽

Species Classification ◽

The Impact

Fish population monitoring systems based on underwater video recording are becoming more popular nowadays, however, manual processing and analysis of such data can be time-consuming. Therefore, by utilizing machine learning algorithms, the data can be processed more efficiently. In this research, authors investigate the possibility of convolutional neural network (CNN) implementation for fish species classification. The dataset used in this research consists of four fish species (Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus), which gives a total of 12859 fish images. For the aforementioned classification algorithm, different combinations of hyperparameters were examined as well as the impact of different activation functions on the classification performance. As a result, the best CNN classification performance was achieved when Identity activation function is applied to hidden layers, RMSprop is used as a solver with a learning rate of 0.001, and a learning rate decay of 1e-5. Accordingly, the proposed CNN model is capable of performing high-quality fish species classifications.

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CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES PERFORMANCE EVALUATION FOR FISH SPECIES CLASSIFICATION

Journal of Sustainability Science and Management ◽

10.46754/jssm.2021.07.010 ◽

2021 ◽

Vol 16 (5) ◽

pp. 124-139

Author(s):

HAMIZAH ISMAIL ◽

◽

AHMAD FAISAL MOHAMAD AYOB ◽

AIDY @ MUHAMED SHAWAL M MUSLIM ◽

MOHAMAD FAKHRATUL RIDWAN ZULKIFLI ◽

...

Keyword(s):

Neural Network ◽

Performance Evaluation ◽

Convolutional Neural Network ◽

Fish Species ◽

Network Architectures ◽

Species Classification ◽

Neural Network Architectures

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Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

Scientific Research Journal ◽

10.24191/srj.v2i2.9330 ◽

2005 ◽

Vol 2 (2) ◽

pp. 25

Author(s):

Noraliza Hamzah ◽

Wan Nor Ainin Wan Abdullah ◽

Pauziah Mohd Arsad

Keyword(s):

Neural Network ◽

Power Quality ◽

Probabilistic Neural Network ◽

Microsoft Excel ◽

Waveform Data ◽

The Neural Network ◽

Harmonic Voltage ◽

Power Quality Disturbances ◽

Electronic Load ◽

Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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