Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images

Compound weighted fusion evaluation and optimization of intelligent tracking algorithm in radar seeker

This paper designs a hierarchical weighted fusion evaluation/optimization scheme for the radar seeker neural network (NN) tracking algorithm. The first weighted fusion of closed-loop performance index is carried out to exclude the hardware influence on algorithm evaluation. Then, according to different tracking scenarios, the tracking index is divided into different periods; a single period score is given by a linear-nonlinear hybrid scoring mechanism. Furthermore, in a single index, the internal scores of different time periods are weighted and fused for the second time to obtain the index overall score. Finally, the third weighted fusion of the multi-index scores obtains the comprehensive score of the algorithm. We design the parameter evaluation case sets and repeat the aforementioned compound weighting; hence the case with the highest comprehensive score is obtained. Finally, the algorithm is optimized by the highest-score case. The experiment using fuzzy NN radar seeker verifies the effectiveness of the method.

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation.