Not all fog removers are equal: Unmasking the impact of dehazing on object detection

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Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
Cilt: 31 Sayı: 3
Not all fog removers are equal: Unmasking the impact of dehazing on object detection

Not all fog removers are equal: Unmasking the impact of dehazing on object detection

Authors : Ahmet Selman Bozkır, Nurçiçek Özenç

Pages : 373-383

View : 23 | Download : 13

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :Dehazing is an important branch of computational photography aiming to enhancing image clarity by removing atmospheric haze and scattering effects, crucial for improving visibility in applications such as unmanned aerial vehicles, traffic control, and autonomous driving. However, most of the studies in this particular field lack an assessment of the developed algorithm in context of object detection (OD). In this study, we aim to quantify and evaluate the contribution of several stateof-the-art dehazing methods (C2PNet, D4, Dehamer, gUNet) on OD using YOLOv8, known for its superior performance. For this purpose, we utilized the test portion of the VisDrone-DET dataset including 548 haze-free aerial images as the data source. For a more comprehensive assessment, we evaluated these approaches to object detection under different haze levels and resolutions. Since it is inherently impossible to obtain hazy and clean images simultaneously, we (1) generated synthetically hazed images involving varying haze densities and (2) resized to 640p and 1280p resolutions. Next, we used YOLO8 and YOLO10 models to evaluate the OD performance in (i) haze-free ground truth, (ii) three different hazed versions, and (iii) their dehazed counterparts through several metrics. Our experiments showed that the gUNET approach, incorporating a variant of the U-Net model inspired by GCANet and GridDehazeNet outperformed the others in terms of OD performance. Surprisingly, the Dehamer negatively affected the OD performance due to the artifacts it produced. This assessment not only provides valuable findings into the effectiveness of these methods but also sheds light on how to benefit them when it comes to object detection under hazy atmospheric conditions.
Keywords : Nesne tespiti, YOLO, İmge sis giderimi, Sentetik sis

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