Abstract
Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.
Highlights
With the application of unmanned aerial vehicles (UAV) in the supervision of ships, forestry, and natural resources [1], the use of UAV in positioning of vessels and management of fishery activities is confirmed [2]
In order to solve the problems of the initialization, the adaptive shape prior method provides a priori trimap to Graphcut [27], which is particular for specific shapes so that this kind of model lacks the generality and ability to detect various shapes of vessels
We input test images to background model, and a template is selected from the library; secondly, a trimap is obtained from the output of the calculating model; we apply the trimap to initialize Grabcut background model, and the target region can be obtained after the segmentation
Summary
With the application of unmanned aerial vehicles (UAV) in the supervision of ships, forestry, and natural resources [1], the use of UAV in positioning of vessels and management of fishery activities is confirmed [2]. Since automated processing often loses recognition accuracy, object recognition and localization have become a hot issue in aerial images on the premise of ensuring high accuracy. For these reasons, we consider that the problem can be regarded as separation of sea foreground and background. Considering the problems mentioned above, we design and propose an improved universal background model based on Grabcut algorithm to separate and identify ship candidate region automatically and quickly. Can the background model guarantee the recognition rate for surface extraction, and it covers the shortage that Grabcut algorithm in the image segmentation process cannot automatically obtain the background results.
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