Author(s)

Dongming Lu^*, Mengke Wang, Xinxin Yang, Longyin Teng, Jiangyun Tan, Zechen Tian, Liping Wang, Guohua Gu

School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.

The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.

Sea Surface Infrared Small Targets, Guided Filtering, Detail Enhancement

Lu, D. , Wang, M. , Yang, X. , Teng, L. , Tan, J. , Tian, Z. , Wang, L. and Gu, G. (2023) A Small Target Detection Method for Sea Surface Based on Guided Filtering and Local Mean Gray Difference. Journal of Computer and Communications, 11, 49-63. doi: 10.4236/jcc.2023.1112004.