Journal ID : TRKU-20-03-2020-10564
[This article belongs to Volume - 62, Issue - 03]
Total View : 200

Title : Performance Comparison of the Deep Segmentation Methods on License Plate Detection

Abstract :

Automatic License Plate Detection and Recognition (ALPD-R) is used in traffic, security and parking management systems. In this paper, deep semantic segmentation methods are used for license plate detection. Three different deep segmentation methods are considered in license plate segmentation. They are SegNet, Fully Convolutional Network (FCN) and Densely-Connected and Concatenated-Multi-Encoder-Decoder (DCCMED) network. These models are further trained on the same training data set and tested accordingly. Visual and numeric evaluations are used to validate the performance of the proposed work. Recall, precision and F-measure values are calculated. According to the obtained results, FCN outperforms the recall, precision and F-measure values are 0.6410, 0.9043 and 0.7491, respectively. The DCCMED produces the second best evaluation scores where obtained scores are 0.4955, 0.9188 and 0.6493. Finally, the worse segmentation scores are produced by SegNet architecture. Its achievements are 0.4916, 0.7704 and 0.5913, respectively. Hence, the DCCMED’s precision value is higher than the SegNet’s and FCN’s precision value

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