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<p>Autonomous-vehicle perception models perform well in clear daytime but degrade under adverse weather and low illumination. We quantify how annotation quality affects YOLO-based 2D detection using the forward-facing RGB subset of the MUSES dataset spanning clear, rain, fog, snow, and night. Focusing on lightweight detectors (YOLOv11n/YOLOv11s) to reflect embedded ADAS constraints, we observe systematic label noise (missing objects, mislocalized boxes, and class inconsistencies) that limits achievable accuracy. We therefore introduce a semi-automatic label-upgrading pipeline: a high-capacity teacher produces candidate labels, which are then verified and corrected by a human annotator. Under identical training settings, retraining on the cleaned annotations improves mAP@0.5 from approximately 0.13 to 0.56–0.62 across conditions, indicating that supervision quality is a major bottleneck for all-weather detection and that data-centric fixes can unlock large gains even for lightweight models.</p>

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Label quality as a bottleneck for YOLO detection in all-weather autonomous driving using the MUSES dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月21日

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  • Dalal Z. Zreiqat,
  • Vinh Nguyen,
  • Nathir A. Rawashdeh

摘要

Autonomous-vehicle perception models perform well in clear daytime but degrade under adverse weather and low illumination. We quantify how annotation quality affects YOLO-based 2D detection using the forward-facing RGB subset of the MUSES dataset spanning clear, rain, fog, snow, and night. Focusing on lightweight detectors (YOLOv11n/YOLOv11s) to reflect embedded ADAS constraints, we observe systematic label noise (missing objects, mislocalized boxes, and class inconsistencies) that limits achievable accuracy. We therefore introduce a semi-automatic label-upgrading pipeline: a high-capacity teacher produces candidate labels, which are then verified and corrected by a human annotator. Under identical training settings, retraining on the cleaned annotations improves mAP@0.5 from approximately 0.13 to 0.56–0.62 across conditions, indicating that supervision quality is a major bottleneck for all-weather detection and that data-centric fixes can unlock large gains even for lightweight models.