Nighttime infrared ship target detection based on Two-channel image separation combined with saliency mapping of local grayscale dynamic range

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The automatic detection of infrared ship targets at night is the key research of intelligent maritime monitoring. Because the infrared maritime small targets are always dim, owning only simple texture, and variable in shape in voyage, deep learning is not so suitable for these kinds of target detection. In this paper, two-channel image separation combined with saliency mapping of local grayscale dynamic range (TCS-SMoLGDR) algorithm for ship target detection is proposed. The saliency mapping of local grayscale dynamic range (SMoLGDR) is proposed by taking full advantage of the feature that the infrared ship target at night owns the largest grayscale dynamic range. A saliency map is generated through SMoLGDR, in which the target area is significantly enhanced and the background is effectively suppressed. On the basis of the saliency map, the connected domain mean strategy is used to determine the real target area, which is beneficial to retain the complete target. For better segmentation of the target with uneven grayscale distribution from the background, this paper proposes a two-channel image separation (TCS) method to separate the local image of the target area into a bright channel image and a dark channel image, so that the grayscale distribution of targets in sub-images becomes relatively uniform. Finally, edge-guided binarization is used to extract the target. The experiment results show fully that the algorithm proposed in this paper can achieve the effective detection of the small dim targets in the nighttime infrared maritime image, and the detection accuracy is better than the comparison algorithms.