Summary of Shm-traffic: Drl and Transfer Learning Based Uav Control For Structural Health Monitoring Of Bridges with Traffic, by Divija Swetha Gadiraju et al.
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic
by Divija Swetha Gadiraju, Saeed Eftekhar Azam, Deepak Khazanchi
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method utilizes deep reinforcement learning-based control for an Unmanned Aerial Vehicle (UAV) to conduct concrete bridge deck surveys while traffic is ongoing, detecting cracks using advanced techniques for structural health monitoring (SHM). The approach employs two edge detection methods: canny edge detection and Convolutional Neural Network (CNN)-based crack detection. Transfer learning is applied with pre-trained weights from a crack image dataset, enabling the model to adapt and improve its performance in identifying and localizing cracks. Proximal Policy Optimization (PPO) is used for UAV control and bridge surveys. The proposed methodology is evaluated across various scenarios, with key metrics such as task completion time and reward convergence observed to gauge its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way for drones to inspect bridges while cars are driving on them. They taught the drone to use two different techniques to find cracks in the bridge deck. One method uses computer vision like the human eye, and the other uses a special kind of artificial intelligence called a neural network. The drone’s control system learns from experience to navigate the bridge safely and efficiently. The researchers tested their approach on different scenarios and found that it can detect cracks quickly and accurately. |
Keywords
» Artificial intelligence » Cnn » Neural network » Optimization » Reinforcement learning » Transfer learning
