CADENet: Asynchronous Enhancement for Adverse Weather Detection
A team of researchers has introduced CADENet, an innovative system designed for camera-based object detection in autonomous vehicles operating under challenging weather conditions like rain, fog, sand, and snow, without the need for training. The system consists of three threads: Thread S utilizes YOLOv11n at full frame rate with no additional latency; Thread Q implements condition-adaptive enhancement (CAPE) and combines results through entropy-guided NMS, allowing Thread S to continue uninterrupted; and Thread E employs CLIP for weather classification without prior training. CADENet aims to overcome the limitations of evaluating detectors on degraded images and is tailored to fulfill stringent real-time demands in safety-critical perception scenarios.
Key facts
- CADENet is a training-free three-thread system
- Thread S uses YOLOv11n for detections at full frame rate
- Thread Q applies CAPE enhancement and EG-NMS fusion
- Thread E uses CLIP for zero-shot weather classification
- Addresses adverse weather: rain, fog, sand, snow
- Tackles evaluation ceiling in degraded image detection
- Aims to meet hard real-time requirements
- Proposed for autonomous driving perception
Entities
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