What question did this study set out to answer?

To enhance object detection accuracy for autonomous vehicles operating in foggy conditions.

April 28, 2026Open Access

Domain-Adaptive Object Detection for Autonomous Vehicles Under Foggy Conditions Using Lightweight Convolutional Neural Networks

Puntos clave

To enhance object detection accuracy for autonomous vehicles operating in foggy conditions.
Developed a domain-adaptive YOLOv8 framework.
Tested performance under dense fog compared to clear-weather training results.
Achieved real-time inference speed during object detection.
Demonstrated meaningful accuracy retention in fog with the adapted model.
Real-time inference speeds maintained even under adverse weather conditions.

Resumen

Reliable object detection under adverse weather conditions remains a fundamental bottleneck in safe autonomous driving. We propose a domain-adaptive YOLOv8- based frame-work that bridges the performance gap between clear-weather training and real-world foggy deployment, achieving meaningful accuracy retention under dense fog with real-time inference speed.

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