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.

翻译：桥梁视觉检测对于保障其安全性和及早识别潜在故障至关重要。通过将无人机与深度学习模型相结合，可以快速、准确地实现该检测过程的自动化。然而，选择一款足够轻量化以集成到无人机中，同时满足严格推理时间和精度要求的合适模型具有挑战性。因此，本研究通过在桥梁细节检测数据集COCO-Bridge-2021+上对属于四个最新YOLO变体（YOLOv5、YOLOv6、YOLOv7、YOLOv8）的23个模型进行基准测试，为推进该模型选择过程做出贡献。通过全面的基准测试，我们确定YOLOv8n、YOLOv7tiny、YOLOv6m和YOLOv6m6是在精度与处理速度之间提供最佳平衡的模型，其mAP@50分数分别为0.803、0.837、0.853和0.872，推理时间分别为5.3ms、7.5ms、14.06ms和39.33ms。我们的研究结果加速了无人机模型选择过程，从而实现更高效、更可靠的桥梁检测。