Detection of ship targets in seas is an important research field in remote sensing image target detection.Onboard ship detectors need to detect targets rapidly under limited resources and time-constraint without the prior information about the type and size of the ship targets as guidance.In detection of vessel formation

different sizes of ships in a scene are normally present.Thus

an onboard ship detection method needs adapt to changeable detection scene. To solve this problem

a novel multi-scale fractal dimension based onboard ship saliency detection algorithm is proposed

which can detect ship targets of different sizes.Analysis of natural texture images showed that the images of the natural scenes fit the fractal Brown random field.The study of images

such as sea waves

clouds

and other natural objects

shows that the natural objects all have fractal characteristics

whereas ships

aircrafts

vehicles

and other man-made objects hardly show any fractal characteristics.Therefore

the fractal difference between natural background and man-made targets can be used for target detection and recognition of the targets.The key problem is how to estimate the fractal dimension accurately.An improved algorithm is proposed considering that the fractal dimension calculation accuracy is affected by the constrained size of boxes of the differential box counting(DBC) algorithm.In the improved algorithm

the numbers of pairs of points of the fitting line are increased

and the fitting error to eliminate the error pairs of points is introduced.Thus

the calculation accuracy of the fractal dimension features is improved.Experimental results show that the improved algorithm is more accurate in the fractal dimension calculation of small images than the classic DBC algorithm.Based on the accuracy improved fractal dimension

a center-surround(c-s) operator based on the detection principle of Itti model is used in the new algorithm to eliminate the natural background and highlight the ship targets simultaneously given that the natural objects show the self-similarity at different scales

which is different from the man-made objects.The two-parameter CFAR detection method is a classical algorithm for ship target detection commonly used in optical remote sensing images.This method is suitable for the detection of complex image objects with local background changes. The proposed algorithm is compared to the two parameter CFAR algorithm.For the different sizes of ship target detection in one scene

the more obvious ship targets are highlighted by the two-parameter CFAR algorithm

and the targets that are considerably different from the obvious ones in size are easily missed.Thus

the detection rate of target detection is reduced.Moreover

ship targets of different sizes are highlighted through the multi-scale approach of the new algorithm and the background is weakened

which is conducive for ship target detection with different sizes.In the experiment

25 remote sensing images are selected.The total number of ship targets in the selected images is 102

and the size of the ship targets varies in a scene

in which the two algorithms are used to detect the ship targets.Compared with the two-parameter CFAR algorithm

the ship detection method based on the multi-scale fractal dimension has a higher detection rate and lower false alarm rate. The proposed detection algorithm based on the multi-scale fractal dimension can realize the detection of differently-sized ship targets in a scene of remote sensing images

thus effectively reducing the false alarm rate of target detection while ensuring a certain detection rate.To cope with the changeable and complex detection scenarios of onboard imaging

the new algorithm

which can adapt to different sizes of ship target detection in one scene

is more flexible and has strong adaptability.