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YOLOv11 Nano improves object detection accuracy and efficiency

By PulseAugur Editorial · [2 sources] ·

A new study introduces YOLOv11 Nano, an updated iteration of the YOLO object detection series, and benchmarks it against YOLOv8 Nano. The research evaluated their performance on a fused dataset combining Indian Driving Dataset and Berkeley Deep Drive Dataset, focusing on mixed traffic scenarios under adverse weather conditions like rain and low light. YOLOv11n demonstrated a 3.2% improvement in precision, achieving a mAP@50 of 46.6%, while also reducing computational load by 22% and maintaining real-time inference speeds of 70.9 FPS on a Tesla T4 GPU. AI

IMPACT YOLOv11 Nano offers improved accuracy and efficiency for object detection, potentially enhancing autonomous driving systems in challenging conditions.

RANK_REASON Academic paper detailing performance analysis of a new model iteration against a baseline.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Quoc Thuan Nguyen, Ha Anh Vu, Ngo Dang Thanh Ngan, Minh Phuc Hoang Ngoc ·

    Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries

    arXiv:2606.12066v1 Announce Type: new Abstract: In modern vehicular systems, robust performance under harsh conditions has become a critical problem of autonomous driving. Our study delivers a comprehensive evaluation of the newest iteration of the YOLO series, which is YOLOv11 N…

  2. arXiv cs.CV TIER_1 English(EN) · Minh Phuc Hoang Ngoc ·

    Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries

    In modern vehicular systems, robust performance under harsh conditions has become a critical problem of autonomous driving. Our study delivers a comprehensive evaluation of the newest iteration of the YOLO series, which is YOLOv11 Nano architecture benchmarked against the widely …

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