对比YOLO系列模型，提出混合架构提升头盔检测精度与效率。
Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis
- 设计混合式YOLO结构，融合多模型优势提升检测性能。
- h-YOLO在mAP、召回率上优于YOLOv8/v9/v11，推理更快。
- 适合交通监控、智能安防等实时头盔检测场景应用。
头盔检测对提升公共道路安全至关重要，属于目标检测任务。本文对比了YOLOv8、YOLOv9及最新发布的YOLOv11在头盔检测中的可靠性与计算开销。此外，提出一种改进的混合架构（h-YOLO），显著提升整体性能。通过标准评估指标如召回率、精确率和mAP（平均精度均值）进行测试，并记录训练与推理时间，全面评估模型在实时检测场景下的表现。实验结果表明，h-YOLO在各项指标上均优于独立模型，具备更强实用性。
原文摘要 · Abstract (English)
Helmet detection is crucial for advancing protection levels in public road traffic dynamics. This problem statement translates to an object detection task. Therefore, this paper compares recent You Only Look Once (YOLO) models in the context of helmet detection in terms of reliability and computational load. Specifically, YOLOv8, YOLOv9, and the newly released YOLOv11 have been used. Besides, a modified architectural pipeline that remarkably improves the overall performance has been proposed in this manuscript. This hybridized YOLO model (h-YOLO) has been pitted against the independent models for analysis that proves h-YOLO is preferable for helmet detection over plain YOLO models. The models were tested using a range of standard object detection benchmarks such as recall, precision, and mAP (Mean Average Precision). In addition, training and testing times were recorded to provide the overall scope of the models in a real-time detection scenario.
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