With the development of intelligent transportation and parking, license plate detection in open environments is in great demand. However, due to the clutter of background and variation of license plates, the existing methods could not make a good balance between accuracy and efficiency. A method based on semantic region proposals is presented. By thinking from the pixel level, this method first adopts a semantic segmentation convolutional network for license plate candidate region extraction. To improve accuracy of segmentation, an enhanced loss function is designed. Afterward, a classification and regression network based on the oriented bounding box regression algorithm is used for region verification and refinement. Experiments on three public datasets show that the proposed method can be adapted to license plate images captured under different scenarios and can achieve better performance than the state-of-the-art methods.
Due to the variation of background, illumination, and view point, license plate detection in an open environment is challenging. We propose a detection method by boundary clustering. To start with, a boundary map is obtained through Canny edge detector and removal of unwanted horizontal background edges. Second, boundaries are classified into different clusters by a density-based approach. In the approach, the density of each boundary is defined by the total gradient intensity of its neighboring and reachable boundaries. Also, the cluster centers and the number of them are determined automatically according to a minimum-distance principle. At last, a set of horizontal candidate regions with accurately located borders are extracted for classification. The classifier is trained on the histogram of oriented gradient feature by a linear support vector machine model. Experiments on three public datasets including images captured under different scenarios demonstrate that the proposed method outperforms several state-of-the-art methods in detection accuracy and its performance in efficiency is also comparable.
KEYWORDS: Image segmentation, Binary data, Simulation of CCA and DLA aggregates, Image quality, Image processing, Sensors, Distortion, Optical character recognition, Detection and tracking algorithms, Intelligence systems
Character segmentation (CS) plays an important role in automatic license plate recognition and has been studied for decades. A method using multiscale template matching is proposed to settle the problem of CS for Chinese license plates. It is carried out on a binary image integrated from maximally stable extremal region detection and Otsu thresholding. Afterward, a uniform harrow-shaped template with variable length is designed, by virtue of which a three-dimensional matching space is constructed for searching of candidate segmentations. These segmentations are detected at matches with local minimum responses. Finally, the vertical boundaries of each single character are located for subsequent recognition. Experiments on a data set including 2349 license plate images of different quality levels show that the proposed method can achieve a higher accuracy at comparable time cost and is robust to images in poor conditions.
Within intelligent transportation systems, fast and robust license plate localization (LPL) in complex scenes is still a challenging task. Real-world scenes introduce complexities such as variation in license plate size and orientation, uneven illumination, background clutter, and nonplate objects. These complexities lead to poor performance using traditional LPL features, such as color, edge, and texture. Recently, state-of-the-art performance in LPL has been achieved by applying the scale invariant feature transform (SIFT) descriptor to LPL for visual matching. However, for applications that require fast processing, such as mobile phones, SIFT does not meet the efficiency requirement due to its relatively slow computational speed. To address this problem, a new approach for LPL, which uses the oriented FAST and rotated BRIEF (ORB) feature detector, is proposed. The feature extraction in ORB is much more efficient than in SIFT and is invariant to scale and grayscale as well as rotation changes, and hence is able to provide superior performance for LPL. The potential regions of a license plate are detected by considering spatial and color information simultaneously, which is different from previous approaches. The experimental results on a challenging dataset demonstrate the effectiveness and efficiency of the proposed method.
License plate localization (LPL) in open environment is quite challenging due to plate variations and environment variations. In this paper a new algorithm for license plate localization based on color pair and stroke width features of character is proposed. Four steps are mainly concerned in our algorithm. The image is first preprocessed by canny edge detector and color pair feature is extracted. And then edge pixels are clustered into several groups using by EM-based method. Further more, stroke width feature of edge pixels in each group are extracted to remove false groups and background outliers. Finally, LP candidates can be formed by morphological operation and prior knowledge of LP is used for verification and accurate location. We use a standard dataset including natural scene images with background noise, various observation views, changing illumination and various plate sizes for testing. The results show that the proposed algorithm achieves accuracy over 90% on localizing license plate and the processing time is 250ms in average for one image with size of 640*480.
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