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Section Speed Measurement for Traffic Analysis
Kubíčková, Pavla ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
This bachelor thesis focuses on section speed measurement for traffic analysis. This thesis desribes existing methods of detection of license plates and classification of their characters. Methods of cascade classifier and classifier SVM are described in this work. Evaluation of individual parts of the system is processed in the final section.
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LPR detection and OCR
Krajíček, Pavel ; Horák, Karel (referee) ; Honec, Peter (advisor)
The theme of this thesi’s deals with the detection and recognition of car license plate from pictures made of screening machine situated on a crassing or inside a car. The thesis si divided into two basic parts. First deals with searching for presence of licence plate in the picture. If the marque was found, we continue the second part of the program which identificates the found license plate. The first part of program aspires to find the licence plate by the edge detectors. The second part classifies characters by the method based on an analytical description.
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Detection of Vehicle License Plates in Video
Líbal, Tomáš ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with preparation of training dataset and training of convolutional neural network for licence plate detection in video. Darknet technology was used for detection, specifically the YOLOv3-tiny neural network model. The solution was focused on the most accurate detection and the smallest number of false positives per image, thus minimizing overall model error. Dataset was prepared from existing freely available datasets, from the dataset provided by the GRAPH@FIT research group, and from self-annotated images created from downloaded YouTube videos. Furthermore, this dataset has been processed using data augmentation, extending it to twice the size. The YOLO Mark tool was used to create annotations. An ROC curve was used to visualize the detection success. Created solution reaches minimum total error 10,849%. Part of the solution is already mentioned dataset.
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Detection of Vehicle License Plates in Video
Líbal, Tomáš ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with preparation of training dataset and training of convolutional neural network for licence plate detection in video. Darknet technology was used for detection, specifically the YOLOv3-tiny neural network model. The solution was focused on the most accurate detection and the smallest number of false positives per image, thus minimizing overall model error. Dataset was prepared from existing freely available datasets, from the dataset provided by the GRAPH@FIT research group, and from self-annotated images created from downloaded YouTube videos. Furthermore, this dataset has been processed using data augmentation, extending it to twice the size. The YOLO Mark tool was used to create annotations. An ROC curve was used to visualize the detection success. Created solution reaches minimum total error 10,849%. Part of the solution is already mentioned dataset.
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Section Speed Measurement for Traffic Analysis
Kubíčková, Pavla ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
This bachelor thesis focuses on section speed measurement for traffic analysis. This thesis desribes existing methods of detection of license plates and classification of their characters. Methods of cascade classifier and classifier SVM are described in this work. Evaluation of individual parts of the system is processed in the final section.
|
|
|
LPR detection and OCR
Krajíček, Pavel ; Horák, Karel (referee) ; Honec, Peter (advisor)
The theme of this thesi’s deals with the detection and recognition of car license plate from pictures made of screening machine situated on a crassing or inside a car. The thesis si divided into two basic parts. First deals with searching for presence of licence plate in the picture. If the marque was found, we continue the second part of the program which identificates the found license plate. The first part of program aspires to find the licence plate by the edge detectors. The second part classifies characters by the method based on an analytical description.
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