Weld seam detection and location method based on depth camera and improved YOLOv5

Maoyong Li; Pengsheng Zhang

doi:10.1117/12.2678657

14 April 2023 Weld seam detection and location method based on depth camera and improved YOLOv5

Maoyong Li, Pengsheng Zhang

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340L (2023) https://doi.org/10.1117/12.2678657
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

For automatic identification and positioning of the weld seam by the robot, this paper proposed a weld seam recognition and localization method based on the RealSense depth camera and the improved YOLOv5. Firstly, the original YOLOv5 model is improved by inserting the coordinate attention model and getting the center point of the weld in the pixel plane according to the prediction frame. The actual position of the weld seam is then calculated by combining the depth information acquired by the RealSense depth camera. The test results show that the mAP index of this training model improves from 82.3% to 90.8%, which is significantly better than the model before the improvement. The maximum error is 2.9mm when identifying and positioning the object at a distance of 300mm, and the error percentage is within 2% when identifying and positioning the object at a distance of 0.3m-2m. This method established the relationship between the weld detection object and the robot position, compensating for manually moving the weld tracking sensor to its working range. It is of reference significance for welding robot automation.

Citation Download Citation

Maoyong Li and Pengsheng Zhang "Weld seam detection and location method based on depth camera and improved YOLOv5", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340L (14 April 2023); https://doi.org/10.1117/12.2678657

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KEYWORDS

Cameras

Detection and tracking algorithms

Feature extraction

RGB color model

Target detection

Visual process modeling

Education and training

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