Road Object Detection In Foggy Complex Scenes Based On Improved Yolov10

Abstract

Foggy weather presents substantial challenges for
vehicle detection systems due to reduced visibility
and the obscured appearance of objects. To
overcome these challenges, a novel vehicle and
Humans detection algorithm based on an improved
lightweight YOLOv10 model is introduced. The
proposed algorithm leverages advanced
preprocessing techniques, including data
transformations, Dehaze Formers, and dark channel
methods, to improve image quality and visibility.
These preprocessing steps effectively reduce the
impact of haze and low contrast, enabling the model
to focus on meaningful features. An enhanced
attention module is incorporated into the
architecture to improve feature prioritization by
capturing long-range dependencies and
contextual information. This ensures that the model
emphasizes relevant spatial and channel features,
crucial for detecting small or partially visible
vehicles in foggy scenes. Furthermore, the feature
extraction process has been optimized, integrating
an advanced lightweight module that improves the
balance between computational efficiency and
detection performance. This research addresses
critical issues in adverse weather conditions,
providing a robust framework for foggy weather
vehicle and Humans detection.