Man-Made Object Recognition from Underwater Optical Images Using Deep Learning and Transfer Learning

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Abstract
With the development of underwater optical sensors, manmade object recognition from underwater optical images has attracted wide attention. Deep learning methods have demonstrated impressive performance in object recognition tasks from natural images. However, it is difficult to collect large-scale labeled underwater optical images for training such a model. Based on the assumption that it is possible to acquire sufficient labeled in-air images, the proposed work leverages a combination of deep learning and transfer learning to develop a novel recognition system for man-made object from underwater optical images. The extracted features from the proposed network have high representative power, and demonstrate robustness in both in-air and underwater imaging modalities. Therefore, our proposed framework has the ability to recognize underwater man-made objects using only labeled in -air images. The results of experiments on simulated data demonstrate that the proposed method outperforms traditional deep learning methods in the task of underwater man-made object recognition.

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