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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 130-139.doi: 10.12133/j.smartag.SA202202006

• Information Processing and Decision Making • Previous Articles     Next Articles

Underwater Fish Species Identification Model and Real-Time Identification System

LI Shaobo1,2,3(), YANG Ling1,2,3, YU Huihui4, CHEN Yingyi1,2,3()   

  1. 1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    2.National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China
    3.Beijing Engineering and Technology Research Centre for the Internet of Things in Agriculture, Beijing 100083, China
    4.School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
  • Received:2021-09-07 Online:2022-03-30


Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27,370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s.

Key words: fish identification model, CNN, model evaluation, Android, Ground-Truth, real-time identification system

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