🔱 SONAR AI - Customs X-Ray Inspection Models

Dr. Abbas Fadel Jassim | 2026

AI-powered customs inspection system for X-ray cargo scanning

📊 Models Performance Summary

#	Model	Task	Classes	F1 Score	Architecture
1	Classification	تصنيف البضاعة	43	94.2%	Swin-V2 Tiny
2	Concealment	كشف الإخفاء	2	98.4%	Swin-V2 Tiny
3	Risk Assessment	تقييم الخطورة	5	97.3%	DaViT Tiny

🔍 Model 1: Classification (43 cargo categories)

Task: Classify X-ray images into 43 different cargo types

Model	Params	Accuracy	F1 Score
🏆 Swin-V2 Tiny	27.6M	94.2%	94.1%
🥈 DaViT Tiny	27.6M	94.2%	94.1%

Per-Class Performance (Swin-V2):

Category	Precision	Recall	F1	Samples
appliances	98.2%	98.2%	98.2%	57
auto_parts	92.6%	94.3%	93.5%	53
bags	100%	100%	100%	12
banana	100%	100%	100%	40
batteries	100%	88.9%	94.1%	9
beverages	71.4%	100%	83.3%	10
cables	100%	94.4%	97.1%	18
canned_food	92.0%	79.3%	85.2%	29
ceramic	97.5%	97.5%	97.5%	40
chemicals	100%	94.9%	97.4%	39
cleaning	91.7%	91.7%	91.7%	24
clothes	91.8%	96.3%	94.0%	81
cooking_oil	76.9%	100%	87.0%	10
cosmetics	100%	94.1%	97.0%	17
electronics	92.7%	87.9%	90.3%	58
fruits	85.7%	100%	92.3%	6
furniture	97.1%	96.6%	96.8%	174
glass	95.2%	100%	97.6%	20
kitchenware	100%	100%	100%	2
lubricants	92.3%	100%	96.0%	12
machinery	87.5%	95.5%	91.3%	66
meat	92.3%	92.3%	92.3%	13
medical	100%	100%	100%	3
milk	98.4%	98.4%	98.4%	62
motorcycle	100%	66.7%	80.0%	6
nuts	92.9%	100%	96.3%	26

Training: 30 epochs, AdamW (lr=1e-4), CosineAnnealing, Albumentations augmentation

🔍 Model 2: Concealment Detection (match vs no_match)

Task: Detect hidden/smuggled items in X-ray containers

Model	Params	Accuracy	F1 Score
🏆 Swin-V2 Tiny	27.6M	98.4%	98.4%
🥈 DaViT Tiny	27.6M	98.4%	98.4%
🥉 EVA-02 Tiny	5.5M	97.8%	97.8%
4️⃣ MaxViT Tiny	30.4M	96.7%	96.8%

Classes:

match: Container contents match declaration (6,348 images)
no_match: Concealed/smuggled items detected (702 images)

⚠️ Model 3: Risk Assessment (5 levels)

Task: Assess risk level of container cargo

Model	Params	Accuracy	F1 Score
🏆 DaViT Tiny	27.6M	97.2%	97.3%
🥈 Swin-V2 Tiny	27.6M	97.2%	97.2%

Risk Levels:

Level	Name (AR)	Name (EN)	Samples	F1 Score
0	آمن	Safe	6,355	99.1%
1-2	منخفض	Low	5	⚠️ Limited
3	متوسط	Medium	387	79.5%
4	عالي	High	297	84.5%
5	حرج	Critical	6	⚠️ Limited

Note: Low and Critical levels have very few training samples.

📁 Files

SONAR-AI-Models/
├── concealment/
│   ├── best_swinv2.pth      (110 MB, 98.4% F1)
│   ├── best_davit.pth       (110 MB, 98.4% F1)
│   ├── best_eva02.pth       (22 MB, 97.8% F1)
│   └── best_maxvit.pth      (122 MB, 96.8% F1)
├── classification/
│   ├── best_swinv2_43cls.pth (110 MB, 94.1% F1)
│   └── best_davit_43cls.pth  (110 MB, 94.1% F1)
├── risk/
│   ├── best_swinv2_risk.pth  (110 MB, 97.2% F1)
│   └── best_davit_risk.pth   (110 MB, 97.3% F1)
└── results/
    ├── classification_cm.png
    ├── classification_training.png
    ├── risk_cm.png
    └── risk_training.png

🛠️ Usage

import timm, torch
from torchvision import transforms
from PIL import Image

# Load model
model = timm.create_model('swinv2_tiny_window8_256', pretrained=False, num_classes=43)
ckpt = torch.load('classification/best_swinv2_43cls.pth', map_location='cpu')
model.load_state_dict(ckpt['model_state_dict'])
model.eval()

# Inference
tf = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])
])
img = tf(Image.open('xray.jpg').convert('RGB')).unsqueeze(0)
with torch.no_grad():
    pred = model(img).argmax(1).item()

📜 License

Apache 2.0

👨‍💻 Author

Dr. Abbas Fadel Jassim - 2026

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