基于YOLOv7的水下泄漏检测机器人研究

Research on underwater leakage detection robot based on YOLOv7

针对当前海底输油管道自主巡管检测和检测漏油点等问题,提出了一种基于深度学习的水下泄漏检测机器人,旨在实现海底输油管道的自主巡检和漏油点的检测。采用SolidWorks对机器人进行了三维设计,对动力系统进行了分析,采用Jeston Nano和STM32单片机进行控制,通过YOLOv7算法实现了泄漏目标检测,对其实现的路径进行详细分析。试验研究结果表明:YOLOv7算法可以实现对表面光滑和表面粗糙的水下管道的检测,模型能够非常好地识别并标记出裂缝结构。该研究为海洋环境保护和能源安全提供了一种可靠的解决方案。

Aiming at the problems of autonomous inspection and oil leak detection of submarine Oil pipeline,an underwater leak detection robot based on deep learning is proposed to realize autonomous inspection and oil leak detection of submarine Oil pipeline.A three-dimensional design of the robot was carried out using SolidWorks,and the power system was analyzed.The Jeston Nano and STM32 microcontroller were used for control.The YOLOv7 algorithm was used to achieve leak target detection,and the implemented path was analyzed in detail.The experimental research results show that the YOLOv7 algorithm can detect underwater pipelines with smooth and rough surfaces,and the model can effectively identify and label crack structures.This study provides a reliable solution for marine environmental protection and energy security.