PFENet: Physics-Informed Frequency-Enhanced YOLO for Object Detection in Hazy Scenes

Object detection technology has been widely applied in fields such as autonomous driving and security surveillance, where it serves as a vital component of intelligent systems. However, under adverse hazy weather conditions, objects obscured by haze and their edges may lose detailed visual information. Existing object detection methods lack targeted mechanisms to address these challenges, resulting in a marked decline in detection accuracy in complex environments. To this end, this paper proposes an object detection method based on an end-to-end robust detection framework, termed the Physics-informed Frequency-Enhanced Network (PFE-Net). We propose a physics-guided visibility enhancement module (PG-VEM) that leverages the atmospheric scattering model and dark channel prior to adaptively compensate for degraded image features under adverse weather conditions, thereby restoring image details and contrast. Meanwhile, the frequency-domain edge-awareness module (FD-EPM) explicitly enhances the geometric contours of blur-obscured objects through Fourier transform and high-pass filtering, thereby improving the discriminability of edge features. Comprehensive experiments were conducted on both the real-world RTTS dataset and the synthetic VOChaze dataset to validate the effectiveness of the proposed approach. The results indicate that the proposed method achieves significant improvements in accuracy, particularly under complex weather conditions, demonstrating excellent all-weather environmental perception capabilities. This method has important practical engineering value and can substantially enhance the safety of autonomous driving and security surveillance systems under adverse weather conditions.