Small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model

• Small object detection in FLIR images with sea clutter is studied in this paper.
• A context-driven Bayesian saliency detection (CBSD) model is proposed.
• CBSD exploits the horizon line as context to reduce detection ambiguity.
• The scale variance problem is also taken into consideration in CBSD.

There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. This paper presents a context-driven Bayesian saliency model to deal with these two issues. By inspecting the camera geometry of the FLIR imaging under the background of sea and sky, we observed that there exists dependency relationship between the locations and scales at which objects may occur, and the context which is defined to be the location of horizon line. Based on this observation, we propose to incorporate contextual information into the basic bottom-up saliency computation, and a unified Bayesian model is developed to achieve this goal. The proposed model is generic and can be potentially applied to other circumstances where context is available for facilitating object detection. Experimental results have demonstrated the effectiveness of our method.