End-to-End United Video Dehazing and Detection
This work addresses video object detection in hazy environments, which is an incremental improvement over existing methods.
The paper tackles the problem of video dehazing and object detection in hazy conditions by proposing an end-to-end network that exploits temporal consistency between frames, resulting in more stable and accurate detection outcomes.
The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.