Title :
Recognition of the type of welding joint based on line structured-light vision
Author :
Wang Xuiping ; Fan Xi ; Fan Ying ; Bai Ruilin
Author_Institution :
Wuxi Prof. Coll. of Sci. & Technol., Wuxi, China
Abstract :
To recognize the type of welding joint is an essential precondition for extracting features of weld seam and guiding robot tracking seam automatically. A method based on a line laser structured-light vision for recognizing the type of welding joint is studied in this paper. Images of welding joint captured by camera are preprocessed firstly for noise reduction and enhancement with wavelet transform, and the reconstructed images are converted to binary ones using appropriate thresholds. Then some features of binary images are further extracted and formed feature vectors which are input into a PNN classifier for classification. Combined with the position relationship of laser and camera, four types of welding joint are eventually recognized. Experimental results show that, this method has a high recognition rate.
Keywords :
feature extraction; image capture; image classification; image denoising; image enhancement; image reconstruction; image segmentation; robot vision; robotic welding; wavelet transforms; welds; Image preprocessing; PNN classifier; camera; feature extraction; feature vectors; image capture; image enhancement; image reconstruction; line laser structured-light vision; noise reduction; robot tracking seam; wavelet transform; weld seam; welding joint type recognition rate; Conferences; Line structured-light; Probabilistic neural network; Wavelet transform; Welding joint;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162700