Industrial Problem Solution
Vehicle license plate recognition using deep learning-based super_resolution algorithm
2020-06-151. Company introduction
Define Co., Ltd. is a company that develops and supplies system software, develops and supplies optimal collection systems for household waste, eradicates harmful tides, and smart parking systems based on deep learning.
2. Problem Background and Summary
Deep Learning-based smart parking system, which is being piloted by Define Co., Ltd., provides services for entry and exit time, vehicle number, real-time parking surface usage, total number of parking spaces, etc.
There is a problem with CCTV images that are being collected, depending on the degree of sunlight or the angle of the vehicle when entering and leaving the vehicle, the recognition rate of the license plate is reduced.
High-accuracy algorithms are required to locate vehicle license plates in CCTV footage for improved service and corporate competitiveness of smart parking systems
In addition, a system is required to extract vehicle numbers so that the parking lot can accurately recognize the time and number of vehicles entering and leaving the vehicle
3. Solving Process
tensorflow object detection API model is compared and analyzed to locate the vehicle license plate.
A new algorithm is needed to extract the vehicle number because we tried to extract the text using the OCR algorithm but could not see the performance as high.
Extracting only the number plate part from the vehicle image using object detection will inevitably cause problems with poor image quality, so a super-resolution algorithm is required to improve image quality.
4. Ripple effects and future plans
In order to locate the license plate of the vehicle, a fast and accurate model was selected and studied among the tensorflow object detection API models.
The test images were selected by comparing the speed and accuracy of each model.
Among algorithms that can extract vehicle numbers, Samsung Electronics presented algorithms that are suitable for image and text analysis.
A super-resolution algorithm for improving image quality of vehicle license plate is presented.
Object detection algorithm and super-resolution algorithm that can be applied for system advancement were proposed.
Analysis based on continuous images rather than single image-based license plate analysis eliminated errors.
1. Company introduction
Define Co., Ltd. is a company that develops and supplies system software, develops and supplies optimal collection systems for household waste, eradicates harmful tides, and smart parking systems based on deep learning.
2. Problem Background and Summary
Deep Learning-based smart parking system, which is being piloted by Define Co., Ltd., provides services for entry and exit time, vehicle number, real-time parking surface usage, total number of parking spaces, etc.
There is a problem with CCTV images that are being collected, depending on the degree of sunlight or the angle of the vehicle when entering and leaving the vehicle, the recognition rate of the license plate is reduced.
High-accuracy algorithms are required to locate vehicle license plates in CCTV footage for improved service and corporate competitiveness of smart parking systems
In addition, a system is required to extract vehicle numbers so that the parking lot can accurately recognize the time and number of vehicles entering and leaving the vehicle
3. Solving Process
tensorflow object detection API model is compared and analyzed to locate the vehicle license plate.
A new algorithm is needed to extract the vehicle number because we tried to extract the text using the OCR algorithm but could not see the performance as high.
Extracting only the number plate part from the vehicle image using object detection will inevitably cause problems with poor image quality, so a super-resolution algorithm is required to improve image quality.
4. Ripple effects and future plans
In order to locate the license plate of the vehicle, a fast and accurate model was selected and studied among the tensorflow object detection API models.
The test images were selected by comparing the speed and accuracy of each model.
Among algorithms that can extract vehicle numbers, Samsung Electronics presented algorithms that are suitable for image and text analysis.
A super-resolution algorithm for improving image quality of vehicle license plate is presented.
Object detection algorithm and super-resolution algorithm that can be applied for system advancement were proposed.
Analysis based on continuous images rather than single image-based license plate analysis eliminated errors.