ArticleDiscover electronics2026
Label quality as a bottleneck for YOLO detection in all-weather autonomous driving using the MUSES dataset.
Article in Discover electronics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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