| Summary: | 碩士 === 中華大學 === 資訊工程學系 === 106 === Recently, the deep learning technologies make the conventional vision-based surveillance technologies getting significant improvement in terms of feature discrimination and recognition accuracy, e.g., the vision-based license plate recognition (LPR) technology. However, the conventional LPR systems still face the serious challenges in the outdoor ill-conditioned environments. In this work, we used WebGL to augment the license plate training database required for all-weather adverse environment. In general, the conventional LPR systems consist of the following modules: feature extraction, license plate locating, character segmentation, and character recognition. However, the performances of these module are strongly correlated with some low level image features, e.g., edges, colors, and textures. These low level image features can be influenced significantly by the illumination and view angle variations of license plates such that the recognition accuracy is degraded. Therefore, this project is expected to the following contributions. First, we apply the WebGL to construct the training database of the ill-conditioned outdoor environments. Second, we used the YOLOv2 DNN architecture to develop deep license plate recognition system in the ill-conditioned environments with recognition accuracy 94%.
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