REALTIME NEURAL NETWORK ALGORITHM FOR FULL-FRAME MARINE SURFACE OBJECTS RECOGNITION

  • V.A. Tupikov SPE "Research and Production Enterprise "Air and Marine Electronics"
  • V.A. Pavlova SPE "Research and Production Enterprise "Air and Marine Electronics"
  • V.A. Bondarenko SPE "Research and Production Enterprise "Air and Marine Electronics"
  • N.G. Holod SPE "Research and Production Enterprise "Air and Marine Electronics"
Keywords: Unmanned marine navigation, automatic detection and recognition, artificial neural net-work, image database

Abstract

The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

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Published
2020-07-10
Issue
No 1 (2020)
Section
SECTION IV. COMMUNICATION, NAVIGATION, AND GUIDANCE