A Dehazing Network and Self-Supervised Transfer Learning Method in Highway Surveillance Scenes

Abstract

This paper focuses on the application of image dehaze algorithms in highway scenarios, proposing a novel dehaze algorithm and a self-supervised transfer learning method for practical highway surveillance applications. The new lightweight dehazing network with the pyramid network structure is designed by combining the information multi-distillation network (IMDN), the channel and pixel attention module. In the deployed highway monitoring application, the self-supervised transfer learning method by proposed by integrating the pre-trained dehazing model with a dynamic target detection network. Through multiple alternating learning processes, the dehazing model continuously transfer and suitable for the current real-world application scenarios. The proposed algorithm is rigorously tested on an RTX 3090 GPU by using several public standard datasets and real-world highway datasets. The results demonstrate that the new algorithm outperforms state-of-the-art algorithms, achieving significantly higher Peak Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM) on the public datasets. Furthermore, the visual quality of the dehazed images from new algorithm after transfer learning is markedly superior compared to other algorithms in the real-world highway scenarios. In terms of speed, the new algorithm exhibits faster inference speed than other comparative algorithms, achieving a frame rate 25 frames per second (FPS) for the $1920\times 1080$ real video. On the 4KID dataset, the inference speed can reach 26ms.