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Underwater target recognition is a challenging task due to the unrestricted environment of the ocean. With large datasets, deep learning methods have been applied with great success to the image recognition of objects in the air. However, it has been observed that deep neural networks (DNNs) easily suffer from overfitting with small samples. Underwater image acquisition always requires much manpower...
The explosive growth of the underwater images make the demand for automatic accurate object detection more and more urgent. In this paper, we introduced a deep but lightweight neural network to detect fishes. It achieved the state-of-the-art accuracy for fish detection on the dataset of ImageCLEF, which includes 24,277 fish images belonging to 12 classes. Compared with the common used detection network,...
In this article we are addressing the problem of automatic detection and classification of underwater mines on images generated by a Synthetic Aperture Sonar (SAS). To tackle this problem, we are investigating the use of Machine Learning techniques, in particular Deep Learning. Using this method we faced two challenges, (i) the availability of a sufficient amount of training data to learn the classification...
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