Vision-Based Vehicle Classification Revolution
VEBZE's sensor-free vehicle class detection distinguishes 7 different vehicle types with 98.4% accuracy using only camera images. No expensive sensors, loop detectors, or manual logging required.
7 Vehicle Classes: What's Detected?
The VEBZE system automatically recognizes these vehicle types:
🚗 Sedan (Passenger Car)
Standard passenger vehicles, from compact to full-size sedan models
🚙 SUV / Crossover
Sport utility vehicles, high-profile vehicles, crossover models
🚚 Truck
Commercial trucks, semi-trucks, large cargo vehicles
🏍️ Motorcycle
Two-wheeled motorized vehicles, scooter, motorcycle, moped
🚌 Bus
Public transport vehicles, city-intercity buses, minibus
🚐 Van / Minivan
Panel vans, passenger vans, minivans, light commercial vehicles
🛻 Pickup
Open-bed pickups, double cabin, single cabin all variants
🎯 Per-Class Accuracy Rates
Sedan: 99.1%
SUV: 98.7%
Truck: 99.3%
Motorcycle: 97.2%
Bus: 99.5%
Van: 97.8%
Pickup: 98.2%
Average: 98.4%
Why Sensor-Free? Problems with Traditional Methods
Vehicle classification traditionally requires expensive sensors. VEBZE overcomes these barriers:
| Method | Cost | Maintenance | Accuracy | Limitations |
|---|---|---|---|---|
| Loop Detector | High | Difficult | ~85% | Road excavation required, sensitive to heavy vehicles |
| Ultrasonic Sensor | Medium | Medium | ~80% | Affected by weather conditions |
| LIDAR | Very High | High | ~95% | Expensive, requires precise calibration |
| VEBZE (Camera) | Low | Minimal | 98.4% | None – only camera required |
Technology: Visual Recognition with Deep Learning
VEBZE's vehicle classification system uses a three-stage deep learning pipeline:
1. Vehicle Detection
In the first stage, all vehicles in the camera image are detected and framed with bounding boxes. Uses YOLO v8 architecture, 85-95ms inference time.
2. Feature Extraction
For each detected vehicle, features are extracted using ResNet-50 backbone. A 512-dimensional feature vector is obtained including vehicle shape, size ratios, wheel configuration, body height.
3. Classification
In the final layer, the feature vector is fed to a 7-class softmax classifier. The class with highest confidence score is selected. Threshold 85% – results below are marked as "uncertain".
🧠 Model Architecture
Training Data: 2.3M images (balanced distribution for 7 classes)
Model Size: 48MB (TensorRT optimized)
Inference Time: 12-18ms (classification layer)
Total Pipeline: 100-115ms (detection + classification)
Use Cases: Where Does It Work?
Vehicle class detection is critically important for many sectors:
🅿️ Smart Parking Management
- Differential Pricing: Higher fee for SUV, discount for motorcycle
- Space Optimization: Directing large vehicles to spacious areas
- VIP Parking: Special areas for specific vehicle classes (high ceiling for pickups)
🛣️ Road Toll Collection
- Class-Based Fee: Automatic fee calculation by vehicle type
- High Accuracy: 98.4% accurate classification without manual verification
- Fast Processing: Recognition even at 100km/h, no traffic flow disruption
🏭 Logistics and Warehouse Management
- Vehicle Type Control: Only truck/van entry, blocking passenger cars
- Loading Bay Assignment: Dock assignment by vehicle size
- Time Optimization: Truck arrival known in advance, preparation done
🚦 Traffic Monitoring and Analytics
- Traffic Composition: Analysis of which vehicle types pass when
- Lane Management: Detection of truck lane violations
- Urban Planning: Road planning decisions based on vehicle type distribution
🏢 Corporate Campus Security
- Visitor Control: Commercial vehicles (truck/van) only to loading area
- VIP Recognition: Executive vehicles (usually sedan/SUV) special parking
- Security Alert: Unexpected vehicle types (bus, truck) trigger alarm
Real-World Success Stories
🅿️ Istanbul Mall - Dynamic Pricing
Problem: Same price for all vehicles, SUVs taking 2 spots
Solution: 50% surcharge for SUV, 50% discount for motorcycle
Result: Revenue +28%, space utilization +40%
🛣️ Ankara Bridge - Automatic Toll
Problem: Manual vehicle type entry, queues, errors
Solution: VEBZE automatic classification, 7 classes
Result: 95% speed increase, error rate -98%
🏭 Izmir Port - Loading Bay Automation
Problem: Trucks going to wrong docks
Solution: Vehicle type detection + automatic routing
Result: Loading efficiency +35%
🚦 Bursa Traffic - Truck Lane Violation
Problem: Trucks using fast lane
Solution: Truck detection + automatic ticketing
Result: Violations -78%, traffic flow improved
Edge vs Cloud: Where is it Processed?
VEBZE vehicle classification runs real-time on edge device:
✅ Edge Processing (VEBZE)
- 100-115ms latency (total)
- No network dependency
- Privacy – images stay on device
- Fixed operational cost (energy)
❌ Cloud Processing
- 500-2000ms latency (including network)
- Internet outage = system fails
- Privacy risk – image goes to cloud
- Variable cost (per API call)
Future: More Classes, Higher Accuracy
VEBZE's vehicle classification model is continuously evolving:
- Sub-Classes: Sedan with compact, mid-size, full-size distinction (2026 Q3)
- Make/Model Recognition: Detect vehicle brand and model (2026 Q4)
- Special Vehicles: Emergency vehicles like ambulance, police, fire (2026 Q2)
- Vehicle Condition: Damage, dirt, modification detection (research phase)
🚗 Test Vehicle Classification
Want to test VEBZE's 7 vehicle class detection at your location? Contact us for demo and see 98.4% accuracy.
Request Free DemoConclusion: Camera is Enough, Sensors Unnecessary
VEBZE detects 7 vehicle classes with 98.4% accuracy using only camera, without expensive sensors. This means both cost savings and high performance:
- No sensor cost (camera already available for ALPR)
- Simple installation and maintenance
- 98.4% average accuracy – better than sensors
- 100-115ms latency – real-time use
- 7 different use scenarios
Image processing and deep learning have surpassed classic sensor technology. Experience the future today with VEBZE.