From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance
This provides a task-oriented benchmark for defogging methods to improve autonomous perception in adverse weather, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper tackled the problem of evaluating defogging methods for autonomous driving by benchmarking their impact on downstream object detection and segmentation performance, revealing when defogging helps and how VLM-based editors compare to dedicated approaches, with strong correlations between VLM judge scores and mAP.
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine details critical for safe operation. While numerous defogging methods exist-from handcrafted filters to learned restoration models-improvements in image fidelity do not consistently translate into better downstream detection and segmentation. Moreover, prior evaluations often rely on synthetic data, leaving questions about real-world transferability. We present a structured empirical study that benchmarks a comprehensive set of pipelines, including (i) classical filters, (ii) modern defogging networks, (iii) chained variants (filter$\rightarrow$model, model$\rightarrow$filter), and (iv) prompt-driven visual--language image editing models (VLM) applied directly to foggy images. Using Foggy Cityscapes, we assess both image quality and downstream performance on object detection (mAP) and segmentation (PQ, RQ, SQ). Our analysis reveals when defogging helps, when chaining yields synergy or degradation, and how VLM-based editors compare to dedicated approaches. In addition, we evaluate qualitative rubric-based scores from a VLM judge and quantify their alignment with task metrics, showing strong correlations with mAP. Together, these results establish a transparent, task-oriented benchmark for defogging methods and highlight the conditions under which preprocessing genuinely improves autonomous perception in adverse weather.