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This research article proposes a framework for automated detection of road defects using machine learning techniques, specifically Long/Short-Term Memory (LSTM) and Random Forest (RF), in conjunction with sensor-enhanced vehicles. The study demonstrates that these models can accurately identify potholes and bumps with an accuracy rate of up to 99.8%, significantly improving the efficiency of road condition monitoring compared to traditional manual methods. By leveraging real-time sensor data, the proposed a

Author

iitdoladeepak

Language

EN