Design and application of pipeline crack detection robot based on YOLOv5 and ROS

This study presents the design and application of a pipeline crack detection robot based on YOLOv5 and ROS, aiming to enhance the accuracy and efficiency of detecting pipeline cracks, a critical factor in industrial safety. Traditional detection methods, such as radiography, ultrasonic, and eddy current testing, face limitations in operational complexity and reliability. By integrating deep learning and robotic technology, this research proposes a novel approach that leverages the YOLOv5 algorithm for precise crack detection and the ROS platform for intelligent robot control. The system incorporates improved YOLOv5 models with enhanced feature extraction and attention mechanisms, achieving a mean Average Precision (mAP) increase of 15% and detection speeds exceeding 30 fps. A multi-modal sensor fusion strategy, combining high-resolution cameras with electromagnetic ultrasonic sensors, boosts detection reliability. Additionally, a reinforcement learning-based path planning strategy optimizes the robot's navigation, increasing coverage by 25%. Experimental results demonstrate a detection accuracy exceeding 95%, with significant reductions in human labor and cost. This integrated system offers a robust solution for pipeline safety, advancing the field of intelligent detection technologies.