EDGE COMPUTING ALGORITHM
Reduce the time and distance of data processing to create maximum efficiency.
Edge Computing Algorithm
Official algorithm partner of international chip makers
With R&D support resources and SDK of all the AI SoC Platform from Qualcomm, Ambarella, MTK, AXIS, Novatek and Realtek, ioNetworks can develop customized applications with high performance to create various AIOT devices.
Various AI algorithms
Customized edge computing algorithms, over 45 recognitions, to various scenarios can be implemented in different AI SoC for multiple functions of AIoT devices.
High accuracy and performance
ioNetworks’ AI models, deeply integrated with platforms of international chip makers, is adjustable to achieve higher accuracy in specific environment and various sites.
Rich experience in cooperation
Solution tuned to perfection based on accumulated experience in numerous projects.
About the Edge Computing
Low Power, Low Bandwidth, Low Latency
Edge Computing processes, analyzes, and stores data directly at the data source; compared to central computing, it consumes only a few watts of energy and reduces the requirements of transmission bandwidth and the delay of time. In order to realize edge computing, ioNetworks’ AI Assessment and Deployment Kit (ADK) could be deployed into the System on a Chip (SoC), so that the computing nodes can be dispersed from the central side to the application side, and the inference can be performed on the device to avoid the delay caused by network transmission and to provide timely alerts and responses.
ioNetworks can select the Edge Computing ADK (Assessment and Deployment Kit) to be embedded into the AI SoC system-on-chip and become an edge computing device according to different fields and needs, which allows the computing nodes to be deployed from the central side to the application side. The detection projects cover over 45 models, including people detection, people counting, loitering detection, object left detection, line crossing detection, camera tampering, vehicle detection algorithms supported for day and night, and more, suitable for various AI devices. Through the collection of site data, it provides real-time multivariate insights and predictive analysis.
Dash Camera
Healthcare
Device
Self-Driving
Vehicle
Locker
Mobile
Home Camera
Webcam
Surveillance
Camera
Conference
Peripheral
Access Control
VR Peripheral
AR Peripheral
MR Peripheral
Devicem for
Medicare
Gaming Peripheral
ioNetworks can select the Edge Computing ADK (Assessment and Deployment Kit) to be embedded into the AI SoC system-on-chip and become an edge computing device according to different fields and needs, which allows the computing nodes to be deployed from the central side to the application side. The detection projects cover over 45 models, including people detection, people counting, loitering detection, object left detection, line crossing detection, camera tampering, vehicle detection algorithms supported for day and night, and more, suitable for various AI devices. Through the collection of site data, it provides real-time multivariate insights and predictive analysis.
Dash Camera
Healthcare Device
Self-Driving Vehicle
Locker
Mobile
Home Camera
Webcam
Surveillance Camera
Conference Peripheral
Access Control
VR Peripheral
AR Peripheral
MR Peripheral
Devicem for Medicare
Gaming Peripheral
The analysis of vehicle type identification, traffic flow, and turning volume calculation is carried out for different usage scenarios. It also extends to the analysis of red-light detection and detention time to adjust the duration of red and green lights and road planning through the data results to solve congestion problems and provide a better driving experience for road users.
License plates appearing on the screen can be correctly recognized while maintaining a high accuracy rate and recognition speed (recognition accuracy can reach over 95% in the daytime and over 85% at night within 0.2 seconds).
Integrated with the management system, it can detect road conditions 24 hours a day. The system will record the violation, location, time, and license plate information, providing the traffic bureau with a large amount of valid violation data. This allows them to make effective traffic planning and manpower arrangements for intersections that are prone to violations, and to reduce the time of traffic police officers.
The analysis of vehicle type identification, traffic flow, and turning volume calculation is carried out for different usage scenarios. It also extends to the analysis of red-light detection and detention time to adjust the duration of red and green lights and road planning through the data results to solve congestion problems and provide a better driving experience for road users.
License plates appearing on the screen can be correctly recognized while maintaining a high accuracy rate and recognition speed (recognition accuracy can reach over 95% in the daytime and over 85% at night within 0.2 seconds).
Integrated with the management system, it can detect road conditions 24 hours a day. The system will record the violation, location, time, and license plate information, providing the traffic bureau with a large amount of valid violation data. This allows them to make effective traffic planning and manpower arrangements for intersections that are prone to violations, and to reduce the time of traffic police officers.