From Fog to Failure: The Unintended Consequences of Dehazing on Object Detection in Clear Images

Ashutosh Kumar, Aman Chadha

Track: long paper (up to 4 pages)

Keywords: dehazing, deep learning, bio-inspired, object detection

TL;DR: Visual cue-based dehazing improves detection in fog but degrades it in clear conditions. We analyze this tradeoff and propose a balanced hybrid pipeline.

Abstract: This study explores the challenges of integrating human visual cue-based dehazing into object detection, given the selective nature of human perception. While human vision adapts dynamically to environmental conditions, computational dehazing does not always enhance detection uniformly. We propose a multi-stage framework where a lightweight detector identifies regions of interest (RoIs), which are then improved via spatial attention-based dehazing before final detection by a heavier model. Though effective in foggy conditions, this approach unexpectedly degrades the performance on clear images. We analyze this phenomenon, investigate possible causes, and offer insights for designing hybrid pipelines that balance enhancement and detection. Our findings highlight the need for selective preprocessing and challenge assumptions about universal benefits from cascading transformations.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 14