对比23个YOLO模型，找出最适合无人机桥检的高效高精度方案。
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis
- 基于四个最新YOLO变体，在桥梁细节数据集上系统评估模型性能。
- YOLOv6m6达到0.872 mAP@50且推理仅39.33ms，兼顾精度与速度。
- 为无人机搭载轻量模型提供实证依据，适合工程部署和快速巡检。
桥梁视觉检测对保障安全、早期发现隐患至关重要。通过集成深度学习模型的无人机（UAV）可实现快速准确的自动化检测。但如何选择既轻量化又满足推理速度与精度要求的模型仍具挑战。为此，本文在新构建的COCO-Bridge-2021+数据集上，对23个最新YOLO系列模型（YOLOv5、YOLOv6、YOLOv7、YOLOv8）进行了全面基准测试。结果表明，YOLOv8n、YOLOv7tiny、YOLOv6m、YOLOv6m6在精度与速度间取得最佳平衡，其mAP@50分别为0.803、0.837、0.853、0.872，推理时间分别为5.3ms、7.5ms、14.06ms、39.33ms。研究为无人机桥检模型选型提供了有力参考，显著提升检测效率与可靠性。
原文摘要 · Abstract (English)
Visual inspections of bridges are critical to ensure their safety and identify potential failures early. This inspection process can be rapidly and accurately automated by using unmanned aerial vehicles (UAVs) integrated with deep learning models. However, choosing an appropriate model that is lightweight enough to integrate into the UAV and fulfills the strict requirements for inference time and accuracy is challenging. Therefore, our work contributes to the advancement of this model selection process by conducting a benchmark of 23 models belonging to the four newest YOLO variants (YOLOv5, YOLOv6, YOLOv7, YOLOv8) on COCO-Bridge-2021+, a dataset for bridge details detection. Through comprehensive benchmarking, we identify YOLOv8n, YOLOv7tiny, YOLOv6m, and YOLOv6m6 as the models offering an optimal balance between accuracy and processing speed, with mAP@50 scores of 0.803, 0.837, 0.853, and 0.872, and inference times of 5.3ms, 7.5ms, 14.06ms, and 39.33ms, respectively. Our findings accelerate the model selection process for UAVs, enabling more efficient and reliable bridge inspections.
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