Fish species recognition using convolutional neural network / Tan Ying Ying

Tan, Ying Ying (2018) Fish species recognition using convolutional neural network / Tan Ying Ying. Masters thesis, University of Malaya.

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      Fish Recognition using machine learning is one of the significant breakthroughs that could be achieved by marine researchers and marine scientists. With the advancement of the machine learning in marine field, some of the problems that perplexed researchers can be solved especially in data collection. Application of machine learning to marine field is still immature, many aspects still need to be improved. Differentiating between two fish species with similar appearance is relatively challenging. On top of that, the angle of fish in the images and the background of the images can cause confusion to the recognition system. Therefore, it is quite challenging to build a fish recognition system. This study focuses on designing a fish recognition system by using Convolutional Neural Network (CNN). The proposed method employs Network-in-Network (NIN) model for fish recognition. NIN model using Multilayer Perceptron (Mlpconv) instead of linear filter and apply Global Average Pooling (GAP) for the last pooling layers. The result of NIN is then compared with a 3 layers CNN. To verify the utility of the proposed model, a set of data is prepared for prediction after training. The performance of the model assessed based on the F1-score of the test data. The accuracy of the developed system is 83%.

      Item Type: Thesis (Masters)
      Additional Information: Research Report (M.A.) - Faculty of Engineering, University of Malaya, 2018.
      Uncontrolled Keywords: Fish Recognition; Machine learning; Convolutional Neural Network (CNN); Network-in-Network (NIN); Multilayer Perceptron (Mlpconv); Global Average Pooling (GAP)
      Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
      Divisions: Faculty of Engineering
      Depositing User: Mrs Rafidah Abu Othman
      Date Deposited: 20 Mar 2019 03:49
      Last Modified: 14 Jul 2021 03:06

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