Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering

It has always been a great challenge to efficiently detect small infrared targets from complex image backgrounds without any prior knowledge. This is especially true when both strong and weak targets appear in the same image or when the weak targets come up on image borders. The main contribution of this paper is to design a robust infrared maritime target detection method, in which a visual attention and pipeline-filtering model is proposed by integrating a revised visual attention model (VAM) and the antivibration pipeline-filtering algorithm. The revised VAM, a single-frame target detection strategy, will first compute a saliency map (SM) from a specific modality, which is automatically selected according to image background smoothness. Then, an automatic strategy for extracting suspected targets from an SM is also proposed here, which highlights targets and suppresses background clutters in SMs through local saliency singularity evaluation. Moreover, contrary to the original VAM, we adopt border saliency preservation in center-surround difference so that robust detection can be guaranteed for targets on image borders. Finally, to eliminate the interference of sea glints and confirm real targets, we adopt the antivibration pipeline-filtering algorithm, a multiframe-based clutter removal method. Compared with the original VAM and two other existing target detection algorithms, experimental results have proven that our strategy can detect infrared maritime targets much better under different environmental conditions. This research can significantly improve the success rate and efficiency of searching maritime targets in different weathers using infrared imager, especially in heavy sea fog and strong ocean waves.