PulseAugur
中
实时 15:15:08
English(EN) Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries

YOLOv11 Nano 提高了目标检测的准确性和效率

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新研究介绍了YOLO目标检测系列的更新版本YOLOv11 Nano，并将其与YOLOv8 Nano进行了基准测试。该研究评估了它们在融合了Indian Driving Dataset和Berkeley Deep Drive Dataset的数据集上的性能，重点关注雨天和低光照等恶劣天气条件下的混合交通场景。YOLOv11n在精度上提高了3.2%，mAP@50达到了46.6%，同时将计算负载降低了22%，并在Tesla T4 GPU上保持了70.9 FPS的实时推理速度。 AI

影响 YOLOv11 Nano在目标检测方面提供了更高的准确性和效率，有可能在挑战性条件下增强自动驾驶系统。

排序理由 学术论文，详细分析了新模型迭代相对于基线的性能。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [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 …

相关话题