Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis
This work addresses the need for efficient model selection in UAV-based bridge inspections, but it is incremental as it benchmarks existing models without introducing new methods.
The paper tackled the problem of selecting lightweight deep learning models for UAV-assisted bridge inspection by benchmarking 23 YOLO variants on a bridge dataset, identifying YOLOv8n, YOLOv7tiny, YOLOv6m, and YOLOv6m6 as optimal with mAP@50 scores up to 0.872 and inference times as low as 5.3ms.
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.