Automatic License Plate Recognition (ALPR) has been an important research topic for many years in the intelligent transportation system and image recognition fields. License Plate (LP) detection and recognition has always been a challenging issue due to several factors, including different weather and lighting, unavoidable data acquisition noise, and requirement for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. Different techniques have been proposed based on machine learning, deep learning, and image processing for the detection and recognition of LPs. This paper proposes a method that performs vehicle LP detection and character recognition with high accuracy by using artificially generated multi-exposure images of the LP. First, one under-exposed and three over-exposed images are generated from a reference image taken from the camera. Then, LP detection and character recognition algorithms are applied on these five images – one real image and four synthesized images. At each character location in LP, the character detected with the highest confidence level among these images is selected as the final predicted character. The system is fully automated, and no pre-processing, calibration, or configuration procedures are needed. Experimental results show that the proposed method achieves high accuracy and works robustly even in challenging conditions. The proposed method can be used in any existing ALPR system and the results on three recent ALPR techniques show that the accuracies are further improved when they are combined with the proposed method.

The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).