Citation

Keh, Zheng Xian and Wong, Lai Kuan and Loh, Yuen Peng and Gu, Ke and Lin, Weisi (2024) KBY-Net: A Dual Learning Framework for Improving Object Detection in Rainy Weather Conditions. In: MMASIA '24: Proceedings of the 6th ACM International Conference on Multimedia in Asia, December 3 - 6, 2024, Auckland New Zealand.

Text KBY-Net_ A Dual Learning Framework for Improving Object Detection in Rainy Weather Conditions.pdf - Published Version Restricted to Repository staff only Download (71MB)

Abstract

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

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Computer vision, image
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	10 Feb 2025 03:19
Last Modified:	10 Feb 2025 03:19
URII:	http://shdl.mmu.edu.my/id/eprint/13410

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