From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection
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 enhanced 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.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionSimilar Papers 제목 키워드 기반
Does Haze Removal Help CNN-based Image Classification?
Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vis…
ClassificationGeneral Classificationimage-classificationImage Classification+2A New Multi-Level Hazy Image and Video Dataset for Benchmark of Dehazing Methods
The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this stud…
SSIMPrior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
Image dehazing is a crucial task that involves the enhancement of degraded images to recover their sharpness and textures. While vision Transformers have exhibited impressive results in diverse dehazing tasks, their quad…
DecoderImage DehazingContrastive Learning for Compact Single Image Dehazing
Single image dehazing is a challenging ill-posed problem due to the severe information degeneration. However, existing deep learning based dehazing methods only adopt clear images as positive samples to guide the trainin…
Contrastive LearningImage DehazingSingle Image DehazingDehazing-NeRF: Neural Radiance Fields from Hazy Images
Neural Radiance Field (NeRF) has received much attention in recent years due to the impressively high quality in 3D scene reconstruction and novel view synthesis. However, image degradation caused by the scattering of at…
3D Scene ReconstructionImage DehazingImage GenerationNeRF+2