Convolutional Neural Network (CNN) Applied to the Risk Analysis of Accidents in Vessels Navigating the Amazon Rivers

Abstract

There are many rules to be followed to assess the safety of navigation, the certifiers and classifiers are responsible for ensuring compliance with all these rules that ensure the integrity of the vessels, however, this is not enough. The Naval District, in which the state of Pará is included, was the first in accidents that occurred in the year 2020 and the third in the year 2021. Due to these accident occurrences, concepts of artificial intelligence, machine learning and deep learning were applied in this area. Aiming to assist in this process, this work proposes to develop an application using Convolutional Neural Network (CNN) for image recognition (Vessels and plimsoll disk). In this sense, a Convolutional Neural Network (CNN) learning technique was used to identify the type of ship through a bank of supplied images, the same method was applied to identify if there is accident risk with the ship through the analysis of plimsoll disk images. To perform the training of the CNNs, six different network architectures were evaluated with: changing the number of filters in each convolutional layer; varying the amount of convolutional layers and; using transfer learning of the VGG-16 network with the fine tuning technique. The results achieved in this work are promising and demonstrate the feasibility of employing Convolutional Neural Network as a method for identifying the images of vessels as from the plimsoll disk).

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