Laser Stripe Segmentation of Weld Seam Based on CNN‑Transformer Hybrid Networks

Fig. 9. Semantic annotation process. (a) Original images; (b) gray scale, thresholded images; (c) Steger operator processed images; (d) manually labeled images

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Fig. 10. CTIM

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Fig. 11. Training results for 20th round of each model

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Table 1. Welding experiment parameters of GMAW
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Table 1. Welding experiment parameters of GMAW
Parameter Value
Dual pulse frequency /Hz 3
Welding current /A 243
Wire feed speed /（m·min^-1） 8.0
Welding speed /（m·min^-1） 0.45
Gas flow /（L·min^-1） 18

Table 2. Ablation test results

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Table 2. Ablation test results

Program	Model	P_PA	P_mPA	P_mIoU	Inference time /ms	Parameter /M
1	MobileViT-S	0.960	0.913	0.852	60	6.4
2	MobileViT-XS	0.948	0.857	0.774	31	2.9
3	MobileViT-XXS	0.912	0.794	0.678	20	1.9
4	CTIM	0.952	0.895	0.819	34	3.4
5	CTIM-DNB	0.964	0.921	0.867	37	3.9
6	CTIM-ESPCN	0.968	0.930	0.874	37	3.7
7	Proposed model	0.980	0.964	0.921	40	4.1

Table 3. Experimental results with different loss functions

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Table 3. Experimental results with different loss functions

Loss function	P_laser	P_PA	P_mPA	P_mIoU	Total training time /s	Stopping round
Dice	0.771	0.956	0.879	0.831	4025.451	43
BCE	0.824	0.964	0.905	0.858	5003.129	57
Jaccard	0.853	0.968	0.920	0.874	4969.245	54
BCE-Dice	0.806	0.960	0.896	0.849	4721.110	50
Focal	0.882	0.972	0.934	0.889	6180.146	78
Proposed loss function	0.941	0.980	0.964	0.921	3679.562	34

Table 4. Test results of different models

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Table 4. Test results of different models

Model	P_PA	P_mPA	P_mIoU	Inference time /ms
DeepLabv3+	0.928	0.850	0.764	80
PSPNet	0.960	0.917	0.852	96
SegNet	0.952	0.888	0.823	88
FCN-32s	0.972	0.948	0.894	110
Unet	0.908	0.834	0.718	68
RefineNet	0.964	0.931	0.867	102
Proposed model	0.980	0.964	0.921	40

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Ying Wang, Sheng Gao, Zhe Dai. Laser Stripe Segmentation of Weld Seam Based on CNN‑Transformer Hybrid Networks[J]. Chinese Journal of Lasers, 2024, 51(24): 2402110

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