An intelligent crash recognition method based on 1DResNet-SVM with distributed vibration sensors

The vibration recognition along the fiber is still a challenge in highway accident monitoring with distributed optical fiber vibration sensing system (DVS). In this paper, a method named 1DResNet-SVM combining 1D residual Neural Network (1DResNet) and support vector machines (SVM) is proposed. One-dimensional raw vibration signals are used to avoid information loss of data. One-dimensional convolution is combined with residual block to solve the degradation of deep network models. Experiments show that the proposed 1DResNet can extract the distinguishable characteristics of DVS vibration signals better than traditional manual feature extraction methods. Then SVM is selected to replace the fully-connected layer in the network which can effectively recognize crash. The proposed 1DResNet-SVM model can achieve an average recognition rate of 96.2%. Three typical events in highway accidents are carried out in field tests, which can reach 100% especially for crash recognition. This paper provides a new method for the identification of crash accidents. •In this paper, we propose a pattern recognition method based on 1DResNet+SVM for highway vehicle collision with guardrail accident.•This paper uses a one-dimensional convolution kernel to replace the original two-dimensional convolution kernel, which avoiding information loss during data processing.•The proposed 1DResNet-SVM model can achieve an average recognition rate of 96.2% for three events, it provides a new idea for the application of distributed optical fiber sensing in the field of traffic safety.