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

With the robust development of deep learning, object detection has gained much attention for practical use cases such as in autonomous driving and surveillance. However, the task is still challenging to the state-of-the-arts in low-light. Consequently, image enhancement has become a common pre-processing step in the pipeline for object detection in low-light environments. Nonetheless, such two-step approach hinges on the reconstruction of the enhanced image which could introduce unseen artifacts and distortion that deteriorates the detection performance instead. Thus, this work proposes a deep enhancement-object features fusion approach to alleviate the problem by infusing deep features extracted from low-light image enhancement with the deep object features of a detection model. It is postulated that features learned by enhancement models emphasizes visual details which were otherwise disregarded by detection models that focus on the abstract appearance of objects. Hence, the fusion of such complementary features would compensate for the details lost due to low-visibility as well as circumvent the reconstruction error for better detection. Specifically, this work performs a study on fusing deep enhancement features from the state-of-the-art Deep Lightening Network (DLN) with the Yolov5 object detection model at various stages. Experiments on the ExDARK dataset showed that such fusion can improve the precision of object detection in various low-light image conditions and outperforms the conventional two-step pre-process-then-detect approach.