Abstract
This work focuses on using advanced techniques for structural health monitoring (SHM) for bridges with Traffic. We propose an approach using deep reinforcement learning (DRL)-based control for Unmanned Aerial Vehicle (UAV). Our approach conducts a concrete bridge deck survey while traffic is ongoing and detects cracks. The UAV performs the crack detection, and the location of cracks is initially unknown. We use two edge detection techniques. First, we use canny edge detection for crack detection. We also use a Convolutional Neural Network (CNN) for crack detection and compare it with canny edge detection. Transfer learning is applied using CNN with pre-trained weights obtained from a crack image dataset. This enables the model to adapt and improve its performance in identifying and localizing cracks. Proximal Policy Optimization (PPO) is applied for UAV control and bridge surveys. The experimentation across various scenarios is performed to evaluate the performance of the proposed methodology. Key metrics such as task completion time and reward convergence are observed to gauge the effectiveness of the approach. We observe that the Canny edge detector offers up to 40\% lower task completion time, while the CNN excels in up to 12\% better damage detection and 1.8 times better rewards.
Abstract (translated)
此工作重点关注使用先进的结构健康监测（SHM）技术对交通量较高的桥梁进行监测。我们提出了使用深度强化学习（DRL）进行无人机（UAV）控制的方案。我们的方法在交通进行时进行混凝土桥面调查并检测裂缝。无人机进行裂缝检测，裂缝的位置最初是不知道的。我们使用了两种边缘检测技术。首先，我们使用canny边缘检测进行裂缝检测。此外，我们还使用卷积神经网络（CNN）进行裂缝检测，并将其与canny边缘检测进行比较。通过应用预训练的权重，使用CNN进行迁移学习，使得模型能够适应并提高其在识别和定位裂缝方面的性能。 对于无人机控制和桥面调查，我们应用了局部策略优化（PPO）。通过在各种场景中进行实验，以评估所提出的方法的有效性。我们观察到，canny边缘检测的平均任务完成时间可降低40%，而CNN在提高至12%的损伤检测和1.8倍奖励方面表现出色。
URL
https://arxiv.org/abs/2402.14757
