Deep Learning Based Vehicle Number Plate Detection Based on Advance Sequential Long Short-Term Memory with Convolutional Neural Network

Abstract

Day by day, increasing roadside unit vehicles create more traffic, accidents, high speed, theft, and serious problems. Identifying the number plates automatically in vehicle boards is difficult because various angles of projection, number plate types, positions, and character styles are tough. Many existing systems formalize automatic number plate recognition systems based on computer-aided solutions with image processing support. The video is fragmented, and pictures are caught at the right point with appropriate lighting and clarity, and standard textual styles are improperly handled. The point is to plan a proficient robotized authorized vehicle recognizable identification system utilizing vehicle plates by analyzing features. Due to increasing pixel intensity noise illumination during segmentation, feature scaling creates more dimension, leading to improper detection accuracy. By addressing this problem, they proposed an Advance Sequential Long Short Term Memory method with a Convolutional Neural Network (ASLSTM-CNN) approach for a vehicle number plate detection and recognition method that can help detect number plates of vehicles. Initially, the number plate video frames will be collected and converted into images from the Standard UCI repository for training and testing, classification, and detection of the images. The next step is pre-processing the images using Sobel's filtering method; canny filters can help reduce the images. Use the Sobel method to find the approximate absolute gradient scale for each point in the image. The canny filter method can detect each edge first, reducing the noises from the images and finding the images to detect the gradient regions. The second step is segmenting the images using enhanced region-based Convolutional Neural Segmentation (ER-CNS) for segment input images based on the areas and then extracting the features based on the segmenting Region using Enhanced Feature Scaled Social Spider Optimization (EFS3O) analysis of the feature weights based on its threshold values and evaluating the maximum support range. ASLSTM-CNN uses the SoftMax Neural Network (ASLSTM-CNN-SN²) to recognize the image region and check the layers estimations. Finally, characters are identified by ASLSTM-CNN; each feature can be efficient in evaluating the images and improve the detection accuracy by up to 95.6%, with a precision rate of up to 9.1% best rate which is better than previous approaches.