CAJ | 학술논문

声纳图像作为探测水下目标的主要工具，常伴有非高斯噪声，传统的目标检测方法无法较好地应用于声纳图像。提出利用随机共振系统对图像进行降噪处理，并提取图像的梯度特征作为支持向量机的输入特征向量，最终实现水下目标的智能检测。仿真实验结果表明，通过调节随机共振系统的参数能够提高目标检测率，并且该方法的计算量小，具有一定的应用前景。
성납도상작위탐측수하목표적주요공구，상반유비고사조성，전통적목표검측방법무법교호지응용우성납도상。제출이용수궤공진계통대도상진행강조처리，병제취도상적제도특정작위지지향량궤적수입특정향량，최종실현수하목표적지능검측。방진실험결과표명，통과조절수궤공진계통적삼수능구제고목표검측솔，병차해방법적계산량소，구유일정적응용전경。
Sonar imaging , as an important tool for underwater target detection , is often corrupted by non-Gaussian noises , thus the conventional object detection algorithms cannot be effectively applied in sonar image processing .This paper proposes a new method in which a stochastic resonance system is applied to preprocess the images and then normed gradients features are input into the support vector machine , so as to intelligently detect underwater targets .The experiment results show that the proposed approach can improve the target detection rate by tuning the parameters of the stochastic resonance system .Moreover, this method has low computational load and relatively broad application prospect .