Online detection of plate weld defects incorporating triple attention mechanism

Table 1. Improvement of test environment configuration for YOLOv5
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Table 1. Improvement of test environment configuration for YOLOv5
软硬件版本
CPU Intel Core i7 13700H@2.4 GHz
内存 32 GB
显卡 NVIDIA GeForce RTX 4060
主板联想LNVNB161216
操作系统 Windows 11
Python Python3.8
Pycharm Community
Pytorch Torch2.3
Anaconda 3

Table 2. Dataset classification number
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Table 2. Dataset classification number
数据类型烧糊烧穿错边未熔合夹渣
训练集 302 236 200 155 134
验证集 70 65 50 45 43

Table 3. Verification results of ablation experiments

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Table 3. Verification results of ablation experiments

算法	P/%	R/%	mAP@0.5/%	mAP@0.5_0.95/%	Param	GFLOPs
YOLOv5	88.9	86.3	88.4	60.1	7038508	16.0
YOLOv5+Triplet Attention	93.4	86.0	89.2	61.3	7332588	20.5
YOLOv5+DySample	89.8	86.0	88.3	59.3	7003359	15.1
YOLOv5+CLLAHead	95.0	82.9	88.2	61.8	7408680	18.5
YOLOv5+Shape-IoU	91.5	87.1	88.7	59.6	7038508	17.2
YOLOv5+DySample+CLLAHead	92.0	87.9	89.1	59.0	7336212	21.2
YOLOv5+Triplet Attention+CLLAHead	92.3	87.7	90.3	59.1	7322380	21.6
YOLOv5+Triplet Attention+DySample	89.8	88.0	88.6	61.2	7066512	16.5
YOLO-TR	92.5	87.4	89.2	60.9	7351244	19.1

Table 4. Experimental results of different algorithms

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Table 4. Experimental results of different algorithms

算法	P/%	R/%	mAP@0.5/%	mAP@0.5：0.95/%		Param	GFLOPs
YOLOv5	88.8	86.3	88.4		60.1	7038508	16.0
YOLOv5-transformer	91.0	82.8	86.6	60.5		7029772	15.6
YOLOv3-tiny	85.4	75.1	80.3	52.2		8680552	12.9
YOLOv3-spp	87.6	77.5	81.4	51.8		62605644	156.2
YOLOv8	90.1	88.0	90.5	60.0		11128293	28.5
YOLOv10	86.9	83.2	88.5	61.7		8040378	24.5
YOLOv11	90.8	81.4	85.9	61.3		2591205	6.4
YOLOv12	86.6	84.5	84.4	60.2		2558093	6.3
YOLO-TR	92.5	87.4	89.2	60.9		7351244	19.1

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Lingyuan MENG, Yingjun LI, Guicong WANG, Yuan LIU, Jialong GAO, Peng XU. Online detection of plate weld defects incorporating triple attention mechanism[J]. Optics and Precision Engineering, 2025, 33(11): 1818

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