Highway Automatic Vehicle Classification Application
An application for highway automatic vehicle classification can be designed to utilize machine learning algorithms computer vision techniques. This would capture real-time video footage from toll gate cameras and process it to identify and classify vehicles based on their size, shape, and other features.
The application could use a combination of image recognition and deep learning models to accurately classify vehicles into different classes such as cars, trucks, motorcycles, buses, etc. left picture is one of application example of our model: QLV26/40-1000 ABGJ. The models would be trained on a large dataset of labeled vehicle images to learn patterns characteristics to each class.
Once the vehicles are classified, the application can calculate the appropriate toll fee based on pre-defined rates for each vehicle class. In addition, it can record vehicle information, such as license plate numbers of timestamps, for further analysis and processing.
Overall, an application for highway automatic vehicle classification can streamline the toll collection process, improve accuracy, and reduce the need for manual intervention.
Beam spacing：40mm Number of optical axes：14 Protection height：520mm light curtain sensor output (OSSD):2 PNP
Beam spacing：30mm Number of optical axes：14 Protection height: 390mm Area sensor output (OSSD):2 PNP
safety relay for Dadisick Safety Light Curtains
Beam spacing：40mm Number of optical axes：16 Protection height：600mm Security light sensor output (OSSD):2 PNP