用量化技术让无人机在低功耗下实时检测裂缝，兼顾精度与效率。
Survey of Quantization Techniques for On-Device Vision-based Crack Detection
- 对比三种量化方法在多个平台的表现，聚焦轻量模型部署。
- 量化感知训练（QAT）使模型准确率接近浮点水平，F1达0.8376。
- 适合做边缘设备上的智能巡检系统，尤其关注资源受限场景。
结构健康监测（SHM）通过及时发现损伤保障基础设施安全与寿命。基于视觉的裂缝检测结合无人机，克服了传统传感器方法的局限，但需在资源受限设备上部署高效深度学习模型。本研究评估了MobileNetV1x0.25和MobileNetV2x0.5两种轻量卷积神经网络，在TensorFlow、PyTorch和Open Neural Network Exchange平台下，采用动态量化、训练后量化（PTQ）和量化感知训练（QAT）三种量化技术。结果表明，QAT持续保持接近浮点精度的表现，如使用Torch-QAT时MBNV2x0.5的F1-score达0.8376；而PTQ虽显著降低内存与能耗，但在TensorFlow中精度下降明显；动态量化保真度高，但在PyTorch部署困难。借助QAT，本工作实现无人机端实时、低功耗裂缝检测，提升SHM的安全性、可扩展性与成本效益，并为不同平台下精度与效率的权衡提供实践参考。
原文摘要 · Abstract (English)
Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection. Vision-based crack detection, combined with UAVs, addresses the limitations of traditional sensor-based SHM methods but requires the deployment of efficient deep learning models on resource-constrained devices. This study evaluates two lightweight convolutional neural network models, MobileNetV1x0.25 and MobileNetV2x0.5, across TensorFlow, PyTorch, and Open Neural Network Exchange platforms using three quantization techniques: dynamic quantization, post-training quantization (PTQ), and quantization-aware training (QAT). Results show that QAT consistently achieves near-floating-point accuracy, such as an F1-score of 0.8376 for MBNV2x0.5 with Torch-QAT, while maintaining efficient resource usage. PTQ significantly reduces memory and energy consumption but suffers from accuracy loss, particularly in TensorFlow. Dynamic quantization preserves accuracy but faces deployment challenges on PyTorch. By leveraging QAT, this work enables real-time, low-power crack detection on UAVs, enhancing safety, scalability, and cost-efficiency in SHM applications, while providing insights into balancing accuracy and efficiency across different platforms for autonomous inspections.
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