KBY-Net: A Dual Learning Framework for Improving Object Detection in Rainy Weather Conditions

Rainy weather conditions significantly degrade image quality, posing a major challenge for object detection tasks. Conventional methods often address this issue through domain adaptation, or the "derain then detect" approach that utilizes image deraining as the preprocessing technique. This paper presents KBY-Net, a novel end-to-end Y-Net architecture that is built upon the YOLOv8 architecture and leverages multi-task learning for concurrent image restoration and object detection. First, KBY-Net incorporates a novel KBY-decoder designed for image deraining. This decoder leverages Cross Stage Partial (CSP) layer and kernel basis attention (KBA) module to improve feature representation. Second, KBY-Net adopted two innovative modules; a multi-Dconv head transposed attention (MDTA) module at the bottleneck and a multi-axis feature fusion (MFF) block at the neck of the Y-Net. The multi-DConv module empowers the model to capture long-range dependencies and complex representations, and the MFF block refines the extracted features – both contribute significantly to accurate object detection in challenging rainy scenes. Empirical evaluations on benchmark rainy datasets demonstrate that KBY-Net outperforms the state-ofthe-art object detection approaches by a significant margin both quantitatively and qualitatively