Adaptive Sampling and Ghost Multi-scale Fusion for Lightweight Weld Defect Detection

doi:10.16182/j.issn1004731x.joss.24-0244

Abstract

Abstract:

To improve the accuracy and speed of welding defect detection and achieve lightweight models, a lightweight weld defect detection network based on YOLOv8, named light adaptive-weight sampling-YOLO (LAW-YOLO), was proposed. A lightweight adaptive weight sampling LAWS module was designed. It constructed an adaptive weight attention feature map by learning the interacting features within the receptive field.An optimized efficient weighted bidirectional feature pyramid network was adopted as the feature extraction backbone in LAW-YOLO. Furthermore, a ghost multi-scale sampling module was designed, and a hybrid attention mechanism was introduced to enhance the detection capability for small-scale defect targets. Experimental results demonstrate that the average precision (mAP0.5) of the proposed approach on the SteelTube dataset reaches 97.6%, with a data processing speed of 91 frames per second. The approach achieves a 5.5% increase in average defect recognition accuracy and a 4.6% improvement in processing speed compared to the original model and maintains high efficiency while reducing computation by 25.3% and model size by 50%, facilitating deployment on edge devices for operational tasks.

Key words: defect detection, YOLOv8, ghost multi-scale convolution, spatial feature of receptive field, hybrid attention mechanism

CLC Number:

TP391.41

Lu Bin, Yang Xuan, Yang Zhenyu, Gao Xiaotian. Adaptive Sampling and Ghost Multi-scale Fusion for Lightweight Weld Defect Detection[J]. Journal of System Simulation, 2025, 37(8): 1978-1990.

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类别名	Label	原样本数	增强后样本数
气孔	Air-hole	1 339	11 018
咬边	Bite-edge	35	625
断弧	Broken-arc	531	6 459
裂纹	Crack	119	1 601
重叠	Overlap	219	4 869
夹渣	Slag-inclusion	136	1 480
未熔合	Unfused	416	3 614
空心珠	Hollew-bead	613	1 510

注意力机制	位置	精确率/%	召回率/%	mAP0.5/%	参数量/10⁶	FLOPs/10⁹
YOLOv8n		92.2	89.7	92.1	3.151 904	8.7
YOLOv8n+RA	Backbone	92.9	90.4	91.8	3.328 976	8.0
YOLOv8n+RA	Neck	93.1	88.2	92.4	3.319 040	8.3
YOLOv8n+CA	Backbone	92.8	91.1	91.8	3.285 792	9.0
YOLOv8n+CA	Neck	91.6	90.2	89.7	3.279 776	9.0
YOLOv8n+ECA	Backbone	92.8	90.8	92.4	3.219 680	8.8
YOLOv8n+ECA	Neck	93.1	91.3	92.6	3.219 680	8.8
YOLOv8n+MLCA	Backbone	92.9	90.8	92.7	3.151 938	8.7
YOLOv8n+MLCA	Neck	93.6	92.8	93.2	3.151 932	8.7

LAWS	GMSC	BIFPN	MLCA	Inner-CIoU	精确率/%	召回率/%	mAP0.5/%	FLOPs/10⁹	参数量/10⁶
					92.2	89.7	92.1	8.7	3.151 904
√					93.8	92.2	92.9	7.9	2.675 848
	√				93.6	90.2	92.7	7.6	2.862 974
		√			94.2	92.7	93.1	6.8	2.031 124
			√		93.6	91.8	92.2	8.7	3.151 932
√	√				94.4	92.6	93.8	7.5	2.387 336
√	√	√			96.1	93.5	96.4	6.5	1.616 692
√	√	√	√		96.4	94.1	97.1	6.5	1.616 795
√	√	√	√	√	96.8	95.3	97.6	6.5	1.616 795

算法名称	mAP0.5/%	精确率/%	召回率/%	参数量/10⁶	FLOPs/10⁹	帧率/(帧/s)
Faster RCNN	85.6	83.1	82.8	41.2	198.5	45
YOLOv5n	86.7	86.9	84.3	1.9	4.5	98
YOLOv5s	88.2	87.1	83.8	7.2	16.5	70
YOLOXs	91.3	89.1	86.9	9.0	26.8	60
YOLOv7-tiny	91.8	91.2	87.3	6.2	13.7	68
YOLOv8n	92.1	92.2	89.7	3.2	8.7	87
LAW-YOLO	97.6	96.8	95.3	1.6	6.5	91