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Research on multiple jellyfish classification and detection based on deep learning

  • 1182: Deep Processing of Multimedia Data
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Abstract

In recent years, there have been frequent jellyfish outbreaks in many offshore areas worldwide, which have severely affected marine fishery production, coastal tourism, coastal industrial cooling water systems, and marine ecology. Achieving the monitoring of jellyfish plays a vital role in solving the problems mentioned above. However, the research on jellyfish is still in the primary stage. Jellyfish detection technology based on deep learning is gradually being applied to jellyfish detection due to its high efficiency and accuracy, but it is not systematic enough and can identify few jellyfish species. So this paper studies a jellyfish detection algorithm based on deep learning. Based on convolution neural network theory and digital image processing technology, 10 species of jellyfish and fish are detected. Because the quality of underwater images affects the detection accuracy, to further improve the accuracy of the detection algorithm, this paper studies the underwater image processing algorithm. Experimental results show that the image quality is better after applying the three algorithms of dark channel prior algorithm, quadratic combining gray world and perfect reflection algorithm, and contrast-limited adaptive histogram equalization algorithm, which is more conducive to detection. Then, deep learning theory is applied to classify jellyfish. By comparing the AlexNet and GoogLeNet backbone networks’ classification results, the accuracy of the jellyfish classification task based on the GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect jellyfish, and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has a higher detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. In addition, we set up a new jellyfish data set, which includes 25,344 images. The images were divided into 11 species, including 10 species of jellyfish and one fish species. The paper’s research lays a theoretical and technical foundation for the subsequent construction of a real-time monitoring system for underwater jellyfish optical imaging, plays an important role in the development of jellyfish monitoring technology, and provides valuable information for marine biologists.

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Acknowledgements

Thank to Professor Paul L. Rosin and Xianfang Sun (Cardiff University) for participating in writing and technical editing of the manuscript. This work was supported by National Key Research and Development Plan (2019YFC1407904) and National Nature Science Foundation of China (61971373) and Natural Science Foundation of Hebei Province - China (F2019203440, C2020203010) and Science and Technology Support Projects of Key research and Development Plans of Qinhuangdao City - China (201801B010). The authors gratefully acknowledge funding support from the China Scholarship Council.

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Authors and Affiliations

  1. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China

    Ying Han, Qiuyue Chang, Shuaimin Ding, Meijing Gao, Bozhi Zhang & Shiyu Li

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  1. Ying Han
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  2. Qiuyue Chang
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  3. Shuaimin Ding
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  4. Meijing Gao
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Correspondence to Meijing Gao.

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Han, Y., Chang, Q., Ding, S. et al. Research on multiple jellyfish classification and detection based on deep learning. Multimed Tools Appl 81, 19429–19444 (2022). https://doi.org/10.1007/s11042-021-11307-y

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  • DOI: https://doi.org/10.1007/s11042-021-11307-y

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