Chinese Journal of Lasers, Volume. 50, Issue 16, 1602108(2023)
Laser Fringe Segmentation and Feature Points Location Method of Weld Image Based on Multi-Task Learning
Fig. 2. Projection model measurement (
Fig. 3. Network structure. (a) Our network; (b)-(d) Detail/Seg Head, ARM, and FFM modules used in the model
Fig. 8. Laser stripe segmentation results. (a) Original images; (b) laser stripe label images; (c) weld seam features extracted by FCN-8s; (d) features extracted by our method with detailed information supervision; (e) features extracted by our method without detailed information supervision
Fig. 9. Location results of weld feature points by DSNT method under different noise interferences, where the green and blue “+” are left and right feature points and yellow “+” is intermediate feature point
Fig. 10. Comparison of feature point location results. (a1)-(a3) Extraction errors of left feature point, intermediate feature point, and right feature point in u-axis direction in weld image, respectively; (b1)-(b3) left feature point, intermediate feature point, and right feature point in v-axis direction in weld image, respectively
Fig. 11. Comparison of location errors of subtask correlation feature points. (a) In u-axis direction; (b) in v-axis direction
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Yigeng Huang, Daqing Wang, Man Jiang, Haoyu Yin, Lifu Gao. Laser Fringe Segmentation and Feature Points Location Method of Weld Image Based on Multi-Task Learning[J]. Chinese Journal of Lasers, 2023, 50(16): 1602108
Category: Laser Forming Manufacturing
Received: Jul. 15, 2022
Accepted: Sep. 13, 2022
Published Online: Jul. 31, 2023
The Author Email: Wang Daqing (dqwang@iim.ac.cn)