Fish recognition from low-resolution underwater images
Xin Sun, Junyu Shi, Junyu Dong, Xinhua Wang · 2016
The limited underwater observation scenarios pose great challenges to the problem of object recognition from the low-resolution underwater images. This paper proposes a framework to explicitly learn the discriminative features from relatively low resolution images, by resorting to deep learning approaches and super-resolution method. Firstly, the framework tackles the problem of limited discriminative information of low resolution images by a single-image super resolution method. Then state-of-the-art deep learning approaches are employed to learn recognition models for the special underwater fish recognition task. The proposed framework can be effectively implemented for real-time underwater object recognition on autonomous underwater vehicles. To verify the effectiveness of our method, experiments on a public underwater image dataset of fishes are carried out. The results show that our framework achieves promising results for fish recognition on underwater image datasets.