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A convolutional neural network for visual object recognition in marine sector

Object detection and recognition are crucial elements of any high level image analysis system. Convolutional Neural Networks (CNNs) or ConvNets have been applied for recognizing the category of the principal entity in an image for several years. One major benefit of convolutional networks is the use of shared weights in the intermediate convolutional layers, which reduces the required memory size and improves performance. In this work, we created a small database of vessels to analyse different aspects of CNN for recognizing the types of marine vessels in sail. A huge network like CNN may possibly be over fitted due to lack of data. We tried to overcome this serious issue by augmenting the data as well as varying the network parameters and achieved an accuracy of 81.6%. This will be further more investigated to improve the total efficacy of the system.