A Fused Multiscale Pictorial Sequence Learning Mechanism Applied to Pavement Detection

Considering that most pavement anomaly detection algorithms are difficult to play a stable role in data related to different distributions of pavement anomalies, this paper proposes a pavement anomaly detection algorithm based on multi-scale fusion time series information. The algorithm iteratively extracts autoregressive latent variables at different time series scales to quantify dynamic regularity information. Then iterative feature set is embedded into the pictographic subsequence learning module to rapidly extract most representative subsequence features through deep learning. The static statistical evaluation value is retained as one of the specific data to assist in identifying the sample, and the two angle data complement each other to maximize the feature information entropy. Using the SVM classifier with the global sequence alignment algorithm as the main kernel function, the data classification module detects anomalies, so that the algorithm can mine valuable feature attributes from multiple angles as much as possible while stably exerting its classification function. The experimental results show that the multi-scale fusion features capture more complete information about the temporal trajectory than the single latent variable feature information, suggesting that the proposed algorithm exhibits better comprehensive performance and generalization ability than unimproved machine learning algorithms and typical deep learning classifiers.