Download PDFOpen PDF in browserParking System License Plate Detection Based on Convolution Neural Networks GPU OptimizationEasyChair Preprint 72054 pages•Date: December 15, 2021AbstractThis paper presents a parking system license plate detection based on convolution neural networks GPU optimization. When number of strides increased 2 by 2 it reduced memory allocation and reduced training time by half, however accuracy is also reduced from 80% to 75.79%. Accuracy is traded-off to avoid running into GPU memory allocation issues. Parking system license plate detection based on convolution neural networks (CNN) uses convolutional layers that are either completely interconnected or max pooled. The convolutional layer performs a convolutional operation on the input before passing the result to the next layer. The network can be much deeper due to this convolutional operation. With this, convolutional neural networks can be effective in image and video recognition, however it requires graphics processing unit GPU optimization to avoid running into memory issues. Keyphrases: Convolution Neural Network, GPU optimization, Parking System License Plate Detection
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