SGS-YOLO: Method for Detecting Dress Code Violations by Airport Security Personnel

Fig. 10. Comparison of detection results between YOLOv8n and SGS-YOLO. (a) Original images; (b) YOLOv8n detection results; (c) SGS-YOLO detection results

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Table 1. Hyperparameter setting
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Table 1. Hyperparameter setting
Parameter name Parameter value
Learning 0.01
Momentum 0.937
Weight decay 0.0005
Batch size 8
Epoch 300

Table 2. Comparison of different attention mechanisms
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Table 2. Comparison of different attention mechanisms
Model Attention mechanism mAP@0.5 /% Params /10⁶ FPS
YOLOv8n 88.4 3.01 92
CA 91.3 3.05 97
SE 89.6 3.01 105
CBAM 90.1 3.07 102
SimAM 92.7 3.01 90

Table 3. Comparison of different loss functions
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Table 3. Comparison of different loss functions
Model Loss function mAP@ 0.5 /% Params /10⁶ FPS
YOLOv8n CIOU 88.4 3.01 92
EIOU 88.7 3.01 92
DIOU 87.8 3.01 97
SIOU 89.1 3.01 101

Table 4. Ablation experiments

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Table 4. Ablation experiments

Scheme	Model	mAP@0.5 /%	Params /10⁶	FLOPs /10⁹	FPS
1	YOLOv8n	88.4	3.01	8.1	92
2	YOLOv8n+SimAM	92.7	3.01	8.1	90
3	YOLOv8n+GSConv	88.6	2.78	7.6	94
4	YOLOv8n+SIOU	89.1	3.01	8.1	101
5	YOLOv8n+SimAM+SIOU	94.2	3.01	8.1	89
6	YOLOv8n+GSConv+VoV-GSCSP	89.2	2.72	7.4	95
7	YOLOv8n+GSConv+SimAM+ SIOU	94.5	2.78	7.6	94
8	YOLOv8n+GSConv+VoV-GSCSP+SimAM+ SIOU	94.7	2.72	7.4	94

Table 5. Comparison of the different detection algorithms

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Table 5. Comparison of the different detection algorithms

Algorithm	P /%	mAP@0.5 /%	Params /10⁶	FLOPs /10⁹	FPS
Faster R-CNN	82.5	79.8	41.3	273.4	17
SSD	6.2	73.6	26.3	62.7	46
YOLOv5s	89.3	86.9	7.21	15.9	78
YOLOv7	88.6	92.5	37.30	105.3	73
YOLOv8n-MS	94.7	93.1	2.91	4.4	95
YOLOv8s	97.4	95.3	11.10	28.7	81
SGS-YOLO	96.2	94.7	2.72	7.4	94

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Zeping Deng, Hui Liu, Jiliang Tu, Shenhui Ye, Naizhi Liao, Guochao Lai. SGS-YOLO: Method for Detecting Dress Code Violations by Airport Security Personnel[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0637010

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