Critical object recognition in underwater environment

Nowadays, ocean exploration is far from complete and the development of suitable recognition systems are crucial, to allow that the robots perform inspection and monitoring tasks in diverse conditions. The online available datasets are incomplete for these kinds of scenarios and, so it is important to build datasets that covered real condition in a simulated environment. Thus, it was developed a dataset with some man-made objects presents in the underwater environment. Moreover, it is also presented the developed method (Convolutional Neural Network) and its evaluation in diverse conditions is performed. It is also presented a comparative analysis and a discussion between the proposed algorithm and the ResNet architecture. The obtained results showed that the developed method is appropriate to classify 7 critical different objects with good performance.