基于L0梯度平滑与图像分块聚类的海天线检测

Sea-sky-line detection base on L 0 gradient smoothing and image segmentation clusters

海天线检测在海洋工程安防活动中具有重要的意义,真实海洋环境中的海天线检测易受云朵、海浪、光照变化、目标遮挡物、边界模糊等外界干扰。为了实现对真实海洋环境中海天线的检测,本研究提出一种基于L0梯度平滑和图像分块聚类的海天线检测算法。首先,对图像进行L0梯度平滑滤波,以增强海天线边缘,弱化非海天线因素干扰;接着,将图像沿着竖直方向分割成若干等宽图像块,以降低整体环境干扰,加强局部海天线检测效果;然后,通过Canny算子和霍夫变换提取每个分割图像块中的直线段;最后,采取K-means聚类算法提取每个图像块中的海天线段,拟合生成完整海天线。实验结果表明,在真实的海天线数据集中,本研究方法获取的矩形框重叠率平均精度为93.22%,角度差平均精度为7.66%,均高于文中选取的近年典型对比算法。满足实际海天线检测抗干扰强、准确率高、适应性广等要求。

Sea-sky-line detection is of great significance in the security of marine engineering activities.Sea-sky-line detection is susceptible to external interference in real marine environment such as clouds,waves,illumination variation,target occlusions and boundary blur,etc.A sea-sky-line detection algorithm based on L 0 gradient smoothing and image segmentation&clusters is proposed.Firstly,the image is filtered by L 0 gradient smoothing to enhance sea-sky-lines edge and weaken the interference of non-sea-sky-lines.Then,the image is segmented into several equal-width image blocks along vertical direction to reduce environmental interference and strengthen the detection effect of local sea-sky-lines.The straight line segments in each segmented image block are extracted by Canny operator and Hough transform.Finally,K-means clustering algorithm is adopted to extract the sea-sky-line in each image block,thus fitting to generate the final sea-sky-line.Experimental results show that average accuracy of bounding box overlap rate is 93.22%and the average accuracy of angle difference ration is 7.66%in the real sea-sky-line dataset,both of which are higher in comparison with typical algorithms selected in recent years.Result meets the requirements of real sea-sky-line detection with characters of strong anti-interference,high accuracy and wide adaptability.