The ship detection technology is of special significance in both military and civilian level. In order to detect ship targets quickly

efficiently and accurately in a wide and complex sea surface environment

a new method for ship detection based on multi-features and multi-scale visual saliency is proposed. This method makes full use of features of the hyper-complex images which can be operated simultaneously in a number of channels

save operation time

and guarantee the characteristics of different scale characteristics. First

the method uses top-hat algorithm for image preprocessing of the original image to suppress the interference of clouds and oil. Secondly

a variety of features are extracted to form hyper-complex images to detect the significance of ship targets. When we get the last saliency map

we segment ships to ensure the target location by using the OTSU algorithm

and then we mark the ship target in the original image. We make the experimental analysis in several sea conditions

and experimental results show that the algorithm can eliminate fog

cloud and grease interfering with accurate detection of ship targets. In this algorithm

true rate meets 96.52% and the false alarm rate is as low as 2.11%. Compared to other saliency detection algorithm in ship detection

this algorithm has obvious advantages.