Sound Based Classification of Studded Tires: Automatic Tire Classification System

Abstract

The use of studded tires causes rutting of asphalt pavements and generates street dust to the environment. The maintenance of paved roads and cleaning of street dust requires resources and causes health risks. These effects are notable especially in spring time when the snow and ice has melted away from road surfaces. In order to predict these phenomena, the number of vehicles using studded tires should be measured continuously. Previously the estimations about the proportions of winter and summer tires have been created based on figures provided by car service companies that offer tire changing services. Occasional hearing based roadside sample surveys have also been made. Unlike the statistics from car service companies, hearing based data collection methods provide location and time specific information about the use of studded tires. Hearing based data collection is a difficult and labour-consuming task and it has not been applied widely. The purpose of this thesis was to find out if an automatic tire classification system could be implemented to collect data about the use of studded tires. A dataset of in-road audio recordings was exploited in the study. The dataset was collected from two measurement sites by using contact microphones under the road pavement. The measuring points were placed next to automatic traffic measurement stations that are used by Finnish Transport Infrastructure Agency in data collection purposes.

Digital signal processing and machine learning was applied in the designing of the tire classification system. A passenger car detector was implemented to restrict the classification only for tires of passenger cars and to determine the exact bypass times of detected vehicles. Feature extraction from the audio data was done according to modeling of the human auditory system. Two versions of the tire classifier were designed, one based on support vector machine and the other on multilayer perceptron. The dataset was annotated by labelling the recordings with the information about the vehicle class and the tire type used in the vehicle. The recordings of passenger cars were used in the training and testing of the classifier-models. The split of data into a training set and test set was done according to recording locations, meaning that data from one location was named as the training set while the remaining data from the other location was used as test set. This way the generalization of the system could be verified as the classifier-models could not learn the recording location-specific factors of the test set during the training. A comparison of the two classifier models was made according to the results of the experiments that were carried out with the test set.

The results of the experiments prove that automatic and instant tire classification is possible with the proposed methods. Both the passenger car detector and the tire classifier performed well in the experiments by scoring about 95% test accuracy. The differences between the results of the classifier models were small. The results imply that the system is able to generalize its knowledge from one recording environment to another without being explicitly trained to do so. However, due to the small amount of measurement sites used in the experiments, it is impossible to make reliable conclusions about general adaptivity of the system without further research. In order to improve the performance and reliability of the system, more data from new measurement sites should be collected in the follow-up research.