Pulse-length-tolerant features and detectors for sector-scan sonar imagery
Pulse-length-tolerant features and detectors for sector-scan sonar imagery
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DOI：
10.1109/joe.2003.819312
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发表时间：
2004-03
影响因子：
4.1
通讯作者：
S. Perry;Ling Guan
中科院分区：
文献类型：
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作者：
S. Perry;Ling Guan
本文提出了一种基于神经网络的系统来检测小的人造物体序列的扇形扫描声纳图像创建使用各种脉冲长度的信号。通过使用安装在船上的实验性扇形扫描声纳系统，可以在150米范围内探测到这种物体。本研究中考虑的声纳系统有三种操作模式，使用每种模式的不同持续时间的声脉冲在距离船舶200、400和800 m的范围内创建图像。在通过补偿血管的运动执行初始清洁操作之后，图像被分割以提取用于分析的对象。从每个对象中提取的一组31个特征进行检查。这些特征包括基本的对象大小和对比度特征、基于形状矩的特征、不变矩以及从每个对象的二阶直方图中提取的特征。然后使用顺序向前选择（SFS）和顺序向后选择（SBS）为每个模式和所有模式选择15个特征的最佳集合。然后，这些特征用于训练神经网络，以检测每种声纳模式下的人造物体。通过添加描述声纳操作模式的特征，训练神经网络以在三种声纳模式中的任何一种模式下检测人造物体。多模式检测器表现非常好，当与专门为每个声纳模式设置训练的检测器相比。所提出的检测器也表现良好，相比，一些统计检测器的基础上相同的一组功能。建议的检测器实现了92.4%的概率检测的平均误报警率为10每幅图像，平均在所有声纳模式设置。
This paper presents a neural-network-based system to detect small man-made objects in sequences of sector-scan sonar images created using signals of various pulse lengths. The detection of such objects is considered out to ranges of 150 m by using an experimental sector-scan sonar system mounted on a vessel. The sonar system considered in this investigation has three modes of operation to create images over ranges of 200, 400, and 800 m from the vessel using acoustic pulses of a different duration for each mode. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery is segmented to extract objects for analysis. A set of 31 features extracted from each object is examined. These features consist of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. Optimal sets of 15 features are then selected for each mode and over all modes using sequential forward selection (SFS) and sequential backward selection (SBS). These features are then used to train neural networks to detect man-made objects in each sonar mode. By the addition of a feature describing the sonar's mode of operation, a neural network is trained to detect man-made objects in any of the three sonar modes. The multimode detector is shown to perform very well when compared with detectors trained specifically for each sonar mode setting. The proposed detector is also shown to perform well when compared to a number of statistical detectors based on the same set of features. The proposed detector achieves a 92.4% probability of detection at a mean false-alarm rate of 10 per image, averaged over all sonar mode settings.