Real-Time Detection of Dynamic Targets in Dynamic Scattering Media

In dynamic scattering media (such as rain, fog, biological tissues, etc.) environments, scattered light causes severe degradation of target images, directly leading to a sudden drop in the detection confidence of target detection models and a significant increase in the rate of missed detections. This is a key challenge in the intersection of optical imaging and computer vision. Aiming to address the problems of poor generalization and slow reasoning speed of existing schemes, we construct an end-to-end framework of multi-stage preprocessing, customized network reconstruction, and object detection based on the existing network framework. First, we optimize the original degraded image through preprocessing to suppress scattered noise from the source and retain the key features for detection. Relying on a lightweight and customized network (with only 8.20 M of parameters), high-fidelity reconstruction is achieved to further reduce scattering interference and ultimately complete target detection. The reasoning speed of this framework is significantly better than that of the existing network. On RTX4060, the network’s reasoning ability reaches 147.93 frames per second. After reconstruction, the average confidence level of dynamic object detection is 0.95 with a maximum of 0.99, effectively solving the problem of detection failure in dynamic scattering media. It can provide technical support for scenarios such as unmanned aerial vehicle (UAV) monitoring in foggy weather, biomedical target recognition, and low-altitude security.