43,502 research outputs found
A Fast Vertical Edge Detection Algorithm for Car License Plate Detection
Recently, License Plate Detection (LPD) has been used in many applications
especially in transportation systems. Many methods have been proposed in order to
detect license plates, but most of them worked under restricted conditions, such as
fixed illumination, stationary background, and high resolution images. LPD plays an
important role in Car License Plate Recognition (CLPR) system because it affects the
system's accuracy.
This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection
Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method.
Pre-processing step is performed in order to enhance and initialize the input image for
the next steps. This step is divided into three processes: First, the color image
conversion to a gray scale image. Second, an adaptive thresholding is used in order to constitute a binarized image. Third, Unwanted Lines Elimination Algorithm (ULEA)
is used in order to enhance the image. The next step is to extract the vertical edges
from the 352x288 resolution image by using VEDA. This algorithm is based on the
contrast between the values in the binarized image. VEDA is proposed in order to
enhance the CLPD method computation time. After the vertical edges have been
extracted by VEDA, a morphological operation is used to highlight the vertical details
in the image. Next, candidate regions are extracted. Finally, the license plate area is
detected.
This thesis shows that VEDA is faster than Sobel operator; the results reveal that
VEDA is faster than Sobel by about 5-9 times, this range depends on the image
resolution. The proposed CLPD method can efficiently detect the license plate area.
The method shows the total time of processing one 352x288 image is 47.7 ms, and it
meets the requirement of real time processing. Under the experiment datasets, which
were taken from real scenes, 579 from 643 images are successfully detected. The
average accuracy of car license plate detection is 90%. For more evaluation and
comparison purposes, the proposed CLPD method is compared with a similar
Malaysian license plate detection method, which is CAR Plate Extraction Technology
(CARPET). This comparison reveled that the CLPD method is more efficient than
CARPET and also has more detection rate
Car license plate detection method for Malaysian plates-styles by using a web camera
Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so
Study of object detection and reading(license plate detection and reading)
Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized.
Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques.
In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique.
Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other
Empirical Study of Car License Plates Recognition
The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
In this paper, we study the task of detecting semantic parts of an object,
e.g., a wheel of a car, under partial occlusion. We propose that all models
should be trained without seeing occlusions while being able to transfer the
learned knowledge to deal with occlusions. This setting alleviates the
difficulty in collecting an exponentially large dataset to cover occlusion
patterns and is more essential. In this scenario, the proposal-based deep
networks, like RCNN-series, often produce unsatisfactory results, because both
the proposal extraction and classification stages may be confused by the
irrelevant occluders. To address this, [25] proposed a voting mechanism that
combines multiple local visual cues to detect semantic parts. The semantic
parts can still be detected even though some visual cues are missing due to
occlusions. However, this method is manually-designed, thus is hard to be
optimized in an end-to-end manner.
In this paper, we present DeepVoting, which incorporates the robustness shown
by [25] into a deep network, so that the whole pipeline can be jointly
optimized. Specifically, it adds two layers after the intermediate features of
a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the
evidence of local visual cues, and the second layer performs a voting mechanism
by utilizing the spatial relationship between visual cues and semantic parts.
We also propose an improved version DeepVoting+ by learning visual cues from
context outside objects. In experiments, DeepVoting achieves significantly
better performance than several baseline methods, including Faster-RCNN, for
semantic part detection under occlusion. In addition, DeepVoting enjoys
explainability as the detection results can be diagnosed via looking up the
voting cues
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