ScholarWorks@동국대학교

초록

Object detection in adverse weather remains challenging due to the simultaneous degradation of visibility, structural boundaries, and semantic consistency. Existing restoration-driven or multi-branch detection approaches often fail to recover task-relevant features or introduce substantial computational overhead. To address this problem, DLC-SSD, a lightweight degradation-aware framework for detecting robust objects in adverse weather environments, is proposed. The framework integrates image enhancement and feature refinement into a single detection pipeline and adopts a hierarchical strategy in which global and local degradations are corrected at the image level, structural cues are reinforced in shallow high-resolution features, and semantic representations are refined in deep layers to suppress weather-induced noise. These components are jointly optimized end-to-end with the single-shot multibox detection (SSD) backbone. In rain, fog, and low-light conditions, DLC-SSD demonstrated more stable performance than conventional detectors and maintained a quasi-real-time inference speed, confirming its practicality in intelligent monitoring and autonomous driving environments.

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