Abstract
Underwater fish recognition has great value for marine ecological monitoring and management. However, the underwater environment poses great challenges due to absorption and scattering of light which leads to interference images obstructing fish species recognition. In this paper, we use the teacher-student model to distill the interference in underwater fish images, which intends to classify the fish species more efficiently. Specifically, the processed fish image and the raw fish image are used to generate the distance matrix separately, and the interference information is distilled at the feature level by reducing the discrepancy of the two distance matrix, which promotes the network to extract discriminative clues. The KL-Divergence is utilized to further lower the noise in the original data distribution. The results of experiments conducted on several datasets verify that the proposed method is effective and outperforms the competitors.
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Acknowledgements
This work was supported by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008 and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (Grant No. 20200009).
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Pang, J., Liu, W., Liu, B., Tao, D., Zhang, K., Lu, X. (2022). Interference Distillation for Underwater Fish Recognition. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_5
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