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<p>Real-time instance segmentation in urban environments remains a critical challenge for autonomous driving systems, where occluded objects, cluttered backgrounds, and dynamic scales demand both high accuracy and computational efficiency. Traditional methods often sacrifice precision for speed or vice versa, failing to address the dual demands of urban scene understanding. Motivated by the need to bridge this gap, we propose PSC-YOLO, a lightweight framework driven by two core design principles: (1) enhancing multi-scale feature learning to resolve occlusion ambiguities and (2) enabling real-time interaction without compromising segmentation quality. Simultaneously, inspired by the adaptability of the Segment Anything Model (SAM), we streamline its mask decoding via architectural, enabling efficient pixel-level reasoning crucial for real-time urban perception. Experiments on urban road datasets demonstrate that PSC-YOLO outperforms YOLOv8n-seg by 2.0% in mask average precision while operating at 91 FPS-4 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11554_2025_1668_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> faster than FastSAM. This work prioritizes the intrinsic requirements of urban perception systems: balancing precision for safety-critical tasks and speed for real-time decision-making, thereby advancing deployable solutions for autonomous vehicles and smart city infrastructure.</p>

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PSC-YOLO: a lightweight model for urban road instance segmentation

  • 期刊论文

  • 发表日期：

    2025年4月5日

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  • Xiaolin Gu,
  • Guofeng Zhang

摘要

Real-time instance segmentation in urban environments remains a critical challenge for autonomous driving systems, where occluded objects, cluttered backgrounds, and dynamic scales demand both high accuracy and computational efficiency. Traditional methods often sacrifice precision for speed or vice versa, failing to address the dual demands of urban scene understanding. Motivated by the need to bridge this gap, we propose PSC-YOLO, a lightweight framework driven by two core design principles: (1) enhancing multi-scale feature learning to resolve occlusion ambiguities and (2) enabling real-time interaction without compromising segmentation quality. Simultaneously, inspired by the adaptability of the Segment Anything Model (SAM), we streamline its mask decoding via architectural, enabling efficient pixel-level reasoning crucial for real-time urban perception. Experiments on urban road datasets demonstrate that PSC-YOLO outperforms YOLOv8n-seg by 2.0% in mask average precision while operating at 91 FPS-4 \(\times\) × faster than FastSAM. This work prioritizes the intrinsic requirements of urban perception systems: balancing precision for safety-critical tasks and speed for real-time decision-making, thereby advancing deployable solutions for autonomous vehicles and smart city infrastructure.