Automated Detection of Road Defects Using LSTM and Random Forest

Road networks represent an essential factor in economic prosperity and well-being. The transporta-tion efficiency and driving safety of road networks are mainly impacted by assorted damages and defects on the road surface. In current traditions, it may take weeks or even months before competent government departments repair such road conditions, fundamentally due to the lack of timely awareness of them. This research proposes a framework for collecting data and analysis using supervised machine learning (ML) techniques, more specifically Long/Short-Term Memory (LSTM), Random Forest (RF), and networked sensor-enhanced vehicles for effective and efficient monitor-ing and assessment of road surface conditions. Data collected in four different scenarios and multiple vehicle data help to discover abnormal defects and verify automated detection. ML techniques were developed to identify bumps and potholes automatically, which are the two significant types of road defects. The experiment results of both LSTM and RF models can automatically identify the bumps and potholes defects with an accuracy rate of up to 99.8%, using a good detection effect. Cross-validation was set to validate the accuracy of the two decision-making strategies, LSTM and RF. It is expected that automation technology can provide both qualitative and quantitative information about road defect conditions and hence enable timely maintenance to improve transportation efficiency and driving safety.