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Real-time fog removal for autonomous vehicles powered by deep learning with CBAM attention
Fog causes hundreds of fatal crashes yearly. NO-FOG-NET restores visual clarity in under 50ms — fast enough for real autonomous driving decisions at the edge.
Camera captures foggy scene. Visibility severely degraded, edges lost, depth ambiguous.
Channel and spatial attention modules identify which features matter most for haze removal.
Lightweight 2M parameter model reconstructs transmission map and recovers the clear scene.
Dehazed image in under 50ms. Edge-deployable on NVIDIA Jetson Nano and equivalents.
Deploy our 2M parameter fog removal model. Fast inference, zero cloud dependency. Built for autonomous systems.
✓ 1,000 inferences/month
✓ 50ms avg latency
✓ REST API access
✓ Community support
✗ No SLA
✓ 50,000 inferences/month
✓ 30ms avg latency
✓ Model export (ONNX)
✓ Priority support (24hrs)
✓ 99.5% uptime SLA
✓ Unlimited inferences
✓ Edge model deployment
✓ On-premise license
✓ Dedicated support
✓ 99.99% SLA + custom terms
JPG, PNG, WEBP — or click to browse
Processing with NoFogNet…
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Forms on clear nights as ground cools. Common in valleys, reduces visibility to near zero by dawn.
Warm moist air over cold surfaces. Persistent along coastal highways.
Moist air forced up terrain. Challenging for autonomous vehicles on mountain passes.
Steam from warm water into cold air. Patchy and unpredictable — hardest to model.
Droplets freeze on contact, coating lenses and sensors. NoFogNet handles the distortion.
Below -30°C water sublimates into ice crystals. Rare but catastrophic for visibility.
Fog-related crashes cost hundreds of lives yearly. Real-time dehazing gives autonomous vehicles clarity beyond human vision.
Confident navigation in fog reduces idling and unnecessary detours, cutting emissions across fleets.
2M parameters runs on NVIDIA Jetson Nano. No cloud dependency — fully on-device inference.
Training consumed 92% less compute than transformer-based alternatives. Small model, large impact.