基于视觉注意机制的视频监控中海上舰船目标检测

当天空、海面和岸上建筑等自然背景比较复杂时，容易干扰视觉注意的对象，而影响舰船目标的检测。本文提出了一种改进的视觉注意模型来检测海上舰船目标，首先利用小波变换获得舰船目标图像的高频特征和低频特征；然后利用改进的Top-hat滤波器抑制云雾和较强的海杂波，采用改进的Gabor滤波器得到方向特征，采用离散矩变换（DMT，discrete moment transform）得到边缘纹理特征；同时将图像进行色彩空间转换，由HSI（Hue-Saturation-Intensity）空间提取图像亮度、色调和饱和度来构成运动特征和颜色特征；最后将各特征图通过加权线性融合得到兴趣图，通过自适应阈值分割出舰船目标区域。实验证明该舰船目标检测算法具有较好的检测效果。

Ship Targets Detection Based on Visual Attention

When the natural backgrounds such as the sky, the sea and the buildings are more complex, it is easy to disturb the objects of visual attention, and affects the detection of ship targets. So in this paper a new ship detection algorithm based on improved visual attention model was proposed. Firstly, low frequency and high frequency feature were extracted by using wavelet transform theory. Then the algorithm used improved Top-hat filter to suppress the cloud and the strong sea clutter. The direction feature was obtained by using the improved Gabor filter, and the edge-texture feature was obtained by using the DMT (discrete moment transform). The color and movement features were also obtained by converting the images from GRB color space to HSI (Hue-Saturation-Intensity) color space. Finally, the saliency map was got by weighted linear combinations, and the target area was segmented by adaptive threshold algorithm. The experimental results show that the ship detection algorithm has good detection effect.