Weld Seam Identification and Tracking of Inspection Robot Based on Deep Learning Network

• Published in: Drones (Basel) Vol. 6; no. 8; p. 216
• Main Authors: Li, Jie, Li, Beibei, Dong, Linjie, Wang, Xingsong, Tian, Mengqian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2022
• Subjects: Adsorption; Algorithms; Automation; Cameras; Climbing; Deep learning; Design; Hough transformation; Identification systems; Image processing; Image segmentation; Inspection; inspection robot; Machine learning; Methods; Motion control; Neural networks; Permanent magnets; Personal computers; Remote control; Robotics; Robots; Seams; Spherical tanks; Tracking; wall-climbing robot; weld seam identification; Welded joints; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones6080216

The weld seams of large spherical tank equipment should be regularly inspected. Autonomous inspection robots can greatly enhance inspection efficiency and save costs. However, the accurate identification and tracking of weld seams by inspection robots remains a challenge. Based on the designed wall-climbing robot, an intelligent inspection robotic system based on deep learning is proposed to achieve the weld seam identification and tracking in this study. The inspection robot used mecanum wheels and permanent magnets to adsorb metal walls. In the weld seam identification, Mask R-CNN was used to segment the instance of weld seams. Through image processing combined with Hough transform, weld paths were extracted with a high accuracy. The robotic system efficiently completed the weld seam instance segmentation through training and learning with 2281 weld seam images. Experimental results indicated that the robotic system based on deep learning was faster and more accurate than previous methods, and the average time of identifying and calculating weld paths was about 180 ms, and the mask average precision (AP) was about 67.6%. The inspection robot could automatically track seam paths, and the maximum drift angle and offset distance were 3° and 10 mm, respectively. This intelligent weld seam identification system will greatly promote the application of inspection robots.