Maritime Infrared Target Detection Using a Dual-Mode Background Model

Abstract

With the rapid development of marine business, the intelligent detection of ship targets has become the key to marine safety. However, it is difficult to accurately detect maritime infrared targets due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection. According to the global contrast of the image, the scene is divided into the sea surface with violent changes and the sea surface with stable changes. In the first stage, the preliminary background model suitable for steadily changing scenes is proposed. The pixel-level foreground mask is generated through the background block filter and the posterior probability criterion. Moreover, the learning rate parameter is adjusted using the detection results of two adjacent frames. In the second stage, the background model suitable for highly fluctuating scenes is proposed. Moreover, the local correlation feature is used to enhance the local contrast of the frame. The experimental results for the different scenes show that the proposed method has a better detection performance than the other comparison algorithms.