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SDR-Net: A Stage-Wise Degradation-Aware Restoration Network for Robust License Plate Recognition in Complex Port Environments

Year: 2026
Type: article
Abstract: Port areas are core hubs for national logistics and high-risk security zones that require constant vehicle access control. However, ensuring the reliability of automatic license plate recognition (ALPR) systems in port environments is severely challenged by complex image degradations, such as dense haze, low light, and motion blur. In this study, we propose a stage-wise degradation-aware restoration network (SDR-Net), which effectively addresses harsh port conditions by sequentially restoring photometric and structural degradations. Particularly, SDR-Net first secures visual cues lost to haze and low light through a photometric restoration module involving a dark-channel-prior-based dehazing and adaptive brightness adjustment. Next, a structural restoration module based on a generative adversarial network featuring edge-guided structural feature blocks and edge-aware refinement blocks is employed to precisely reconstruct character strokes and outlines damaged by motion blur, stably restoring license plate legibility even under complex degradation conditions. Experiments across various intensities of complex degradation demonstrate that SDR-Net maintains high character recognition accuracies of over 97.35% under mild motion blur and low-concentration haze conditions, indicating its superiority over state-of-the-art models. Notably, the performance gap between SDR-Net and comparison models widened as the degradation intensity increased, and SDR-Net achieved the highest multiscale structural similarity index scores across all intervals.
Source: Mathematics
DOI: 10.3390/math14060934
Authors: Hyungseok Kim, Sungan Yoon, Jeongho Cho
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7134954646
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