Development of Image Analysis Methods for Detecting Nonhomogeneity and Anomalies in the Marine Environment

This paper presents a modified image and video analysis model designed to detect nonhomogeneity and anomalies in the marine environment. Unlike known alternatives, the model can detect natural objects after being trained on scarce data and having access to limited computational resources, in anomalous visual monitoring data collected directly in the marine environment. Accuracy in terms of type I and II errors has been improved by implementing additional preprocessing using intelligent anomaly detection and elimination methods. Requirements for the diversity of training sets have been lowered significantly, whilst the use of invariant metrics with adaptive thresholds improves detection adequacy in cases of affine distortions of scaling and rotation of monitored objects. The paper further compares the results of numerical experiments run on real-world data of in-situ monitoring of marine objects.