In real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. Specifically, fog reduces visibility, complicating feature extraction and leading to detail loss, which impairs object localization and classification. Traditional approaches often apply image dehazing techniques before detection to enhance degraded images; however, these processed images often retain a rough appearance with a loss of detail. To address these challenges, we propose a novel network, DehazeSRNet(DSNet), which is designed to optimize feature transmission and restore lost image details. First, DSNet utilizes the dehaze fusion network (DFN) to learn dehazing features, applying differentiated processing weights to regions with light and dense fog. Second, to enhance feature transmission, DSNet introduces the MistClear Attention (MCA) module, which is based on a re-parameterized channel-shuffle attention mechanism and effectively optimizes feature information transfer and fusion. Finally, to restore image details, we design the hybrid pixel activation transformer (HPAT), which combines channel attention and window-based self-attention mechanisms to activate additional pixel regions. Experimental results on the Foggy Cityscapes, RTTS, DAWN, and rRain datasets demonstrate that DSNet significantly outperforms existing methods in accuracy and achieves exceptional real-time performance, reaching 78.1 frames per second (FPS), highlighting its potential for practical applications in dynamic environments. As a robust detection framework, DSNet offers theoretical insights and practical references for future research on object detection under adverse weather conditions.
Keywords: Adverse weather; Feature fusion; Image processing; Object detection.
© 2025. The Author(s).