面向水下管网的视觉检测系统研究

针对水下管网定期需要检修和维护的实际需求,设计一套水下管网视觉跟踪检测系统;针对水下光学图像特点和管网铺设特征,提出一种管线跟踪方法。方法通过自适应直方图均衡化将光照不均水下图像转换为匀光图像,结合色彩空间变换方式进行图像增强并完成管道区域分割,通过支持向量机的模式识别方法依据水下机器人跟踪策略对管道图像进行分类,并根据管线分类特征建立管线提取策略,基于Kalman滤波对管线进行跟踪处理。在实验水池进行管道跟踪检测实验,管网路径识别正确率达93.7%,验证了视觉检测系统的有效性和稳定性,能够满足水下管网检测实际需求。

Research on visual inspection system for underwater pipeline network

In response to the actual needs of regular maintenance and maintenance of underwater pipe networks, a set of underwater pipeline network visual inspection system is designed. A pipeline tracking method is proposed according to the characteristics of underwater optical images and the characteristics of pipe network laying. The method uses adaptive histogram equalization to convert the unevenly illuminated underwater image into a uniform light image, and combines the color space transformation method to enhance the image and complete the pipeline region segmentation. The pattern recognition method of the support vector machine is used to classify the pipeline image according to the underwater robot tracking strategy, and the pipeline extraction strategy is established according to the classification characteristics of each pipeline. Finally, Kalman filtering is used to track pipelines to ensure system stability. The pipeline tracking detection experiment was carried out in the experimental pool, and the correct rate of pipeline network path identification reached 93. 7%, which verified the effectiveness and stability of the visual inspection system and can meet the actual needs of underwater pipeline network inspection.