An autonomous underwater vehicle (AUV) can operate automatically with a small number of humans and requires few resources when compared to a remotely operated vehicle (ROV). The pipeline inspection with the use of an AUV can be significantly cheaper than those of an ROV. Successful deployment of AUVs relies on accurate pipeline detection and extraction that can automatically locate and track a pipeline direction in real time. We proposed to use forward-looking sonar images to locate and extract pipeline paths since sonar can easily image through turbulence and small particles, commonly found in underwater environments. However, sonar images are prone to disturbance from the environment. Thus, as a superior alternative, we developed a pipeline detection algorithm by considering that a pipeline has a piecewise linear shape. Next, the self-organizing map (SOM) is employed to join the labeled segments together to form a pipeline track to extract the pipeline path for the navigation of an AUV since SOM can map a 2-D image of pipeline into a 1-D line. In our experiment, data obtained from the Gulf of Thailand are used for the performance analysis, and we are able to extract and describe a pipeline direction with accuracies between 90% and 97%.

中文翻译：

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一种使用自组织图的前视声纳图像管道提取算法

与遥控水下航行器 (ROV) 相比，自主水下航行器 (AUV) 可以在少量人员的情况下自动运行，并且需要的资源很少。使用 AUV 进行管道检查可以比 ROV 便宜得多。AUV 的成功部署依赖于准确的管道检测和提取，可以实时自动定位和跟踪管道方向。我们建议使用前视声纳图像来定位和提取管道路径，因为声纳可以轻松地通过湍流和小颗粒成像，这在水下环境中很常见。然而，声纳图像容易受到环境的干扰。因此，作为更好的替代方案，我们通过考虑管道具有分段线性形状来开发管道检测算法。下一个，自组织图 (SOM) 用于将标记的段连接在一起以形成管道轨迹，以提取用于 AUV 导航的管道路径，因为 SOM 可以将管道的二维图像映射为一维线。在我们的实验中，从泰国湾获得的数据用于性能分析，我们能够提取和描述准确度在 90% 到 97% 之间的管道方向。