Detecting ship targets distributed in infrared images of clouds, waves and other complexdisturbances in an unknown complex background, and determining the true ship target in the casewhere the target and the false alarm are very similar are widely used and challenging tasks.This paper proposes an infrared ship targets detection algorithm based on Bayesian theory andSVM combination. Bayesian theory can be used to estimate the probability of partial unknowns withincomplete information. Bayesian formula can be used to correct the probability of occurrence, andfinally we can use the expected value and the modified probability to make the optimal decision. At thesame time, support vector machine (SVM) is a novel small sample learning method with solidtheoretical foundation. It does not involve probability measures and laws of large numbers, so it isdifferent from existing statistical methods.Firstly, the paper introduces the infrared ship target detection method based on Bayesian theory.Then, the image is post-processed to remove redundant false alarm targets. Finally, the paperintroduces the experimental data and the performance evaluation indicators of ship detection results,and compares with other ship detection methods to obtain experimental results.
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