Cloud-Based Collaborative Road-Damage Monitoring with Deep Learning and Smartphones

Abstract

Road damage such as potholes and cracks may reduce ride comfort and traffic safety. This influence can be prevented by regular, proper monitoring and maintenance of roads. Traditional methods and existing methods of surveying are very time-consuming, expensive, require a lot of human effort, and, thus, cannot be conducted frequently. A more efficient and cost-effective process is required to augment profilometer and traditional road-condition recognition systems. In this study, we propose deep-learning methods using smartphone data to devise a cost-effective and ad-hoc approach. Information from sensors on smartphones such as motion sensors and cameras are harnessed to detect road damage using deep-learning algorithms. In order to give heuristic and accurate information about the road damage, we used a cloud-based collaborative approach to fuse all the data and update a map frequently with these road-surface conditions. During the experiment, the deep-learning models achieved good prediction accuracy on our dataset, and the cloud-based fusion approach was able to group and merge the detections from different vehicles.

Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach

“…Chen et al [14] proposed a reflectometry method, to realize real-time potholes observation, with vibration signals analysis and spatio-temporal trajectory fusion. Akshatha et al [38] used a cloud-based collaborative approach, to fuse the results of acceleration-based detection and vision-data-based detection. So we propose a method to identify potholes by fusing acceleration data with video data, inspired by the work of Dong et al [39].…”

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

“…The LSTM network was chosen because it features feedback connections in contrast to traditional feed-forward neural networks. Both individual data points and complete data sequences, such as time series motion data, can be processed according to Ramesh et al [12] and Sepp Hochreiter et al [23]. Since a single motion data point cannot identify specific road surface conditions, this specialization is crucial for our strategy.…”

“…Some cloud computing-based technologies have been used previously to monitor road conditions. Ramesh et al [12] developed a cloud-computing-based road condition monitoring technique using motion and vision data from smartphones. Ameddah et al [13] applied a similar cloud-based technique to detect road conditions with good precision in less time.…”