Improved weld surface defect detection algorithm from YOLOv8

Table 1. Loss function comparison
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Table 1. Loss function comparison
IoU loss function mAP0.5/% R/% Params/M
CIoU 94.6 89.3 2.89
SIoU 92.9 85.1 2.89
GIoU 94.0 88.9 2.89
EIoU 93.3 90.3 2.89
DIoU 93.8 91.7 2.89
WIoU 95.0 89.4 2.89
Inner-SIoU 95.3 89.4 2.89

Table 2. Ablation experiments results

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Table 2. Ablation experiments results

C2f-DWR	CARAFE	GSConv	Inner-SIoU	GD-CBAM	mAP0.5/%	P/%	R/%	FLOPs/G	Params/M	FPS
					87.5	85.5	81.4	8.1	3.01	247
√					95.1	91.1	88.9	8.0	2.95	286
	√				94.2	90.6	88.7	8.4	3.15	132
		√			94.4	89.9	90.1	7.3	2.79	273
				√	90.9	89.1	88.3	8.1	3.01	259
√				√	95.9	92.0	89.5	11.6	3.35	318
√			√	√	96.5	92.3	89.9	11.6	3.35	322
√	√	√		√	94.6	91.2	89.3	7.4	2.89	243
√	√	√	√	√	95.3	90.7	89.4	7.4	2.89	257

Table 3. Results of comparison experiments

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Table 3. Results of comparison experiments

Model	mAP0.5/%	P/%	R/%	FLOPs/G	Params/M	FPS
YOLOv8n(baseline)	87.5	85.5	81.4	8.1	3.01	247
Faster R-CNN	79.8	83.1	77.9	33.3	41.20	95
YOLOv3-tiny	93.4	93.0	88.1	19.0	12.13	242
YOLOv5n	85.7	84.5	78.7	7.1	2.51	282
YOLOv6n	85.0	84.2	78.1	11.8	4.23	285
YOLOv9t	87.4	85.2	80.1	7.6	1.97	229
YOLOv10n	94.3	88.3	88.6	6.5	2.26	292
RT-DETR	90.7	89.5	82.9	110.2	32.01	117
YOLOv11n	94.2	89.1	89.6	6.3	2.58	307
Ours	95.3	90.7	89.4	7.4	2.89	257

Table 4. Experimental results of NEU-DET dataset

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Table 4. Experimental results of NEU-DET dataset

Model	mAP0.5/%	P/%	R/%	FLOPs/G
YOLOv5m	74.8	71.2	69.0	48.0
YOLOv7	73.7	66.3	68.6	104.7
YOLOv8s	74.7	69.1	69.0	28.4
YOLOv8n	77.3	71.9	70.0	8.1
YOLOv9t	73.8	69.7	70.3	7.6
YOLOv11n	77.9	73.9	74.6	6.5
RT-DETR	76.7	72.4	70.2	110.2
WFRE-YOLOv8s	79.4	73.6	75.9	32.6
Ours	78.8	75.2	75.7	7.4

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Runmei Zhang, Chenfei Pan, Zihua Chen, Zhong Chen, Bin Yuan. Improved weld surface defect detection algorithm from YOLOv8[J]. Opto-Electronic Engineering, 2025, 52(3): 240296

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