Chen Xintao, Qiu Changzhen, Zhang Zhiyong
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel). 2023 Aug 21;23(16):7309. doi: 10.3390/s23167309.
Infrared ship target detection is crucial technology in marine scenarios. Ship targets vary in scale throughout navigation because the distance between the ship and the infrared camera is constantly changing. Furthermore, complex backgrounds, such as sea clutter, can cause significant interference during detection tasks. In this paper, multiscale morphological reconstruction-based saliency mapping, combined with a two-branch compensation strategy (MMRSM-TBC) algorithm, is proposed for the detection of ship targets of various sizes and against complex backgrounds. First, a multiscale morphological reconstruction method is proposed to enhance the ship targets in the infrared image and suppress any irrelevant background. Then, by introducing a structure tensor with two feature-based filter templates, we utilize the contour information of the ship targets and further improve their intensities in the saliency map. After that, a two-branch compensation strategy is proposed, due to the uneven distribution of image grayscale. Finally, the target is extracted using an adaptive threshold. The experimental results fully show that our proposed algorithm achieves strong performance in the detection of different-sized ship targets and has a higher accuracy than other existing methods.
红外舰船目标检测是海洋场景中的关键技术。在整个航行过程中，舰船目标的尺度会发生变化，因为舰船与红外摄像机之间的距离在不断改变。此外，复杂的背景，如海面杂波，会在检测任务中造成显著干扰。本文提出了一种基于多尺度形态学重建的显著性映射方法，并结合双分支补偿策略（MMRSM-TBC）算法，用于检测各种大小的舰船目标以及在复杂背景下的舰船目标。首先，提出了一种多尺度形态学重建方法，以增强红外图像中的舰船目标并抑制任何无关背景。然后，通过引入具有两个基于特征的滤波模板的结构张量，我们利用舰船目标的轮廓信息并进一步提高其在显著性映射中的强度。之后，由于图像灰度分布不均匀，提出了一种双分支补偿策略。最后，使用自适应阈值提取目标。实验结果充分表明，我们提出的算法在检测不同大小的舰船目标方面具有强大的性能，并且比其他现有方法具有更高的准确率。
