Weld Seam Detection Method with Rotational Region Proposal Network

The traditional weld seam detection methods in passive vision are usually realized by detecting edges or textures of the weld seam. Since illumination conditions, weld seam types and backgrounds vary with tasks, these methods only work with specific weld types or environment. This paper raises a weld seam detection method that replaces the four main steps in traditional weld seam detection (pretreatment, thresholding, seam detection, seam fitting) with an end-to end neural network system. The method eliminates the ambiguity of original horizontal bounding box by adding an inclination parameter to the region proposal network (RPN). Compared with other methods in passive vision, our method is appropriate for accurate and fast detection of various types of weld seams in complex environment and meets the requirements for online seam detection of industrial robot.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  De Xu,et al.  Visual seam tracking system for butt weld of thin plate , 2010 .

[3]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[4]  Bibhuti Bhusan Biswal,et al.  Advances in weld seam tracking techniques for robotic welding: A review , 2019, Robotics and Computer-Integrated Manufacturing.

[5]  Yiping Yang,et al.  Rotated region based CNN for ship detection , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[6]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Yanling Xu,et al.  Real-time image processing for vision-based weld seam tracking in robotic GMAW , 2014 .

[8]  Tao Lin,et al.  Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding , 2010 .

[9]  Xiangyang Xue,et al.  Arbitrary-Oriented Scene Text Detection via Rotation Proposals , 2017, IEEE Transactions on Multimedia.

[10]  Yanling Xu,et al.  Computer vision technology for seam tracking in robotic GTAW and GMAW , 2015 .

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[15]  De Xu,et al.  A Vision-Based Self-Tuning Fuzzy Controller for Fillet Weld Seam Tracking , 2011, IEEE/ASME Transactions on Mechatronics.

[16]  Tao Lin,et al.  Seam Tracking and Dynamic Process Control for High Precision Arc Welding , 2011 .

[17]  Wei Li,et al.  R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection , 2017, ArXiv.

[18]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[21]  Dong Du,et al.  A Vision Based Detection Method for Narrow Butt Joints and a Robotic Seam Tracking System , 2019, Sensors.