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Most existing methods for automatic license plate recognition (ALPR) focus on a specific license plate (LP) type, but little work focuses on multiple or mixed LPs. This paper proposes a single neural network called ALPRNet for detection and recognition of mixed style LPs. In ALPRNet, two fully convolutional one stage object detectors are used to detect and classify LPs and characters simultaneously, which are followed by an assembly module to output the LP strings. ALPRNet treats LP and character equally, object detectors directly output bounding boxes of LPs and characters with corresponding labels, so they avoid the recurrent neural network (RNN) branches of optical character recognition (OCR) of the existing recognition approaches. We evaluate ALPRNet on a mixed LP style dataset and two datasets with single LP style, the experimental results show that the proposed network achieves state-of-the-art results with a simple one-stage network.INDEX TERMS ALPRNet, license plate recognition, object recognition, convolutional neural network.

Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of COVID-19, we propose a new method that identifies rich features of lung infections from a chest CT image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.

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