DEVELOPMENT OF A DEEP LEARNING BASED VEHICLE LICENSE PLATE DETECTION SCHEME

📄 Project Abstract

This research developed a license plate and classification scheme using deep learning architecture which utilized transfer learning using pre-trained Convolutional Neural Network (CNN). The developed scheme used images obtained from Caltech dataset, Peking University VehicleID (PKU VehicleID) dataset and a developed dataset of vehicle licence plates from Ahmadu Bello University, Zaria called the ABU dataset. De-noising, downscaling operation and grayscale conversion was applied on the acquired image to reduce the cost inqured in using the original image. Sobel operation was performed to detect the edge of the pre-processed image. Edge density filtering and connected component analysis were used to extract and verify the region which constituted the licence plate number. AlexNet model pre-trained on ImageNet was used to extract features and classify the detected license plate candidate's regions. The performance of the developed scheme was evaluated on 150 images of each dataset of PKU ...