This paper discusses the research in small target detection in infrared images with heavy clutter background. For most infrared images, ship objects are rather dim in the relative dark sea surface background. The existence of scan line disturbance and noise also increases the difficulty in proper detection. Dim objects must be distinguished from a dark background. On the other hand, the small targets must also be distinguished from clutters. Through analysis of the targets and background, we build characteristic models of small ship objects, noise and sea backgrounds respectively, and indicate their differences in spatial and frequency domains among them. Based on the principles of signal processing, pattern recognition and artificial intelligence, we propose a combined algorithm for detecting sea surface small targets. In this algorithm, components of background and noise are first suppressed by a multilevel filter designed accordingly, meanwhile enhancing the target ones of interest. The pixels of the candidate targets are then discriminated by minimum risk Bayes test. Finally, according to a priori knowledge about the targets such as the ranges of their sizes, the targets of interest can be detected. In particular, the related probability distributions used by statistic decision are obtained by offline learning of typical training samples. Experiments show that the algorithm is excellent for such kinds of target detection and is robust to noise.
FA-YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background