Hui Li

Hui Li, Chunhua Shen

In this work, we tackle the problem of car license plate detection and recognition in natural scene images. Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition. Firstly, we train a $37$-class convolutional neural network (CNN) to detect all characters in an image, which results in a high recall, compared with conventional approaches such as training a binary text/non-text classifier. False positives are then eliminated by the second plate/non-plate CNN classifier. Bounding box refinement is then carried out based on the edge information of the license plates, in order to improve the intersection-over-union (IoU) ratio. The proposed cascade framework extracts license plates effectively with both high recall and precision. Last, we propose to recognize the license characters as a {sequence labelling} problem. A recurrent neural network (RNN) with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring context information and avoiding errors caused by segmentation, the RNN method performs better than a baseline method of combining segmentation and deep CNN classification; and achieves state-of-the-art recognition accuracy.

Yong Huang, James L. Beck, Stephen Wu et al.

The application of compressive sensing (CS) to structural health monitoring is an emerging research topic. The basic idea in CS is to use a specially-designed wireless sensor to sample signals that are sparse in some basis (e.g. wavelet basis) directly in a compressed form, and then to reconstruct (decompress) these signals accurately using some inversion algorithm after transmission to a central processing unit. However, most signals in structural health monitoring are only approximately sparse, i.e. only a relatively small number of the signal coefficients in some basis are significant, but the other coefficients are usually not exactly zero. In this case, perfect reconstruction from compressed measurements is not expected. A new Bayesian CS algorithm is proposed in which robust treatment of the uncertain parameters is explored, including integration over the prediction-error precision parameter to remove it as a "nuisance" parameter. The performance of the new CS algorithm is investigated using compressed data from accelerometers installed on a space-frame structure and on a cable-stayed bridge. Compared with other state-of-the-art CS methods including our previously-published Bayesian method which uses MAP (maximum a posteriori) estimation of the prediction-error precision parameter, the new algorithm shows superior performance in reconstruction robustness and posterior uncertainty quantification. Furthermore, our method can be utilized for recovery of lost data during wireless transmission, regardless of the level of sparseness in the signal.