| Summary: | Vehicle tires must be maintained to assure performance, efficiency, and safety. Though vehicle owners may monitor tread depth and air pressure, most are unaware of the safety risks of degrading rubber. This paper identifies the need for tire material condition monitoring and develops a densely connected convolutional neural network to identify cracking from smartphone photographs. This model attains an accuracy of 81.2% on cropped outsample images, besting inexperienced humans’ 55% performance. We develop a web service using this model as the basis of an AI-backed “Diagnostics-as-a-Service” platform for online vehicle condition assessment. By encoding knowledge of visual risk indicators into a neural network model operable from a user’s trusted smartphone, we raise awareness of the risk of degraded rubber and improve vehicle safety without requiring specialized operator training.
|
|---|