Specification
|
Item No. |
Voltage |
Power |
Motor speed |
Time for down/up |
Noise |
Temperature |
Protection class |
Certification |
|
CB-N |
DC24V |
100W |
200r/min |
6s |
≤56dB |
-45℃-+65℃ |
IP44 |
CCC, CE |
Features
- Can work continuously, it will not be stop because of the temperature.
- In case of power failure, the barrier be opened and closed manually after release clutch.
Advantages
- The new orange color adds safety and high temperature paint does not fade.
- The chassis is made of specially treated steel plates.
- Extremely cost-effective, and very durable.
- Turboworm integrated movement, motor has overcurrent, overheating and other self-protection functions.
- With stop locking function, can prevent external force lifting rod.
- Advanced elastic balancing mechanism is adopted to make the brake lever lift and fall flat.
- The whole machine has stable operation, low noise, high efficiency and higher reliability.
The Automatic Barrier Systems' License Plate Recognition Technology Principle
One of the most important components of contemporary intelligent transportation systems is license plate recognition technology. Its primary function is to automatically recognize a vehicle's license plate number. Parking lot management, urban traffic monitoring, highway toll collecting, and other industries make extensive use of this technology. The fundamental ideas of license plate recognition technology are as follows:
Obtaining images
Taking a high-definition camera picture of the car is the first step in the license plate identification process. These cameras can clearly record photos of license plates and are typically mounted on the side or above the gate. The camera also has an infrared fill light to guarantee image quality at night or in low light conditions.
Preprocessing images
The original image must be preprocessed because it might have noise, blur, or uneven illumination. Grayscale, binarization, denoising, edge detection, and other preprocessing techniques are used to improve the features of the license plate region and facilitate further recognition processing.
License plate positioning
The license plate area is mixed in with other sections of the car in the preprocessed image rather than being alone. As a result, a number of algorithms are required to determine the license plate's location. Color model-based techniques, Hough transform, template matching, and other techniques are often employed license plate positioning algorithms. Based on the color, form, and texture characteristics of the license plate, these algorithms are able to distinguish it from the background.
Segmenting characters
Segmenting each character on the license plate is the next step after locating the license plate area. For later character identification, this stage is essential. Character segmentation typically divides a license plate image into separate character images using techniques including connected domain analysis, vertical projection, and horizontal projection.
Identification of characters
The foundation of license plate identification technology is character recognition. Typically, there are two steps in this process: feature extraction and classification recognition. Each character image's shape and texture are represented by features that are taken out during the feature extraction stage. Geometric moments, Zernike moments, Fourier descriptors, and other techniques are often employed feature extraction techniques. To determine the precise content of each character, the extracted features are fed into a trained classifier - such as a support vector machine (SVM), neural network, decision tree, etc. - during the classification recognition stage.
Output of results
The entire license plate number is then created by combining the recognized characters and comparing it with the data in the database. The car is permitted to pass if the license plate number matches the database record; if not, an alert may sound or entry may be refused.
The fundamental idea behind license plate recognition technology is as follows. As machine learning and artificial intelligence technologies advance, so does the precision and effectiveness of license plate recognition.
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