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
Customized edge computing algorithms, over 45 recognitions, to various scenarios can be implemented in different AI SoC for multiple functions of AIoT devices.
Rich experience in cooperation
ioNetworks’ AI models, deeply integrated with platforms of international chip makers, is adjustable to achieve higher accuracy in specific environment and various sites.
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
Home Camera
Webcam
Surveillance Camera
Conference Peripheral
Access Control
VR Peripheral
AR Peripheral
MR Peripheral
Devicem for Medicare
Gaming Peripheral
↗ Event Trigger increase New Demand
↗ Enhance User Experience with AI features
↗ Shipment Growth with Affordable Price
↗ Security assessment and protection before data generation to improve data security.
↙ Reduce Power Consumption (ESG)
↙ Reduce Cost (Bandwidth, storage, Cloud Computing)
↙ Reduce Lantency (only 50 ms to complete recognition)
↗ Event Trigger increase New Demand
↗ Enhance User Experience with AI features
↗ Shipment Growth with Affordable Price
↗ Security assessment and protection before data generation to improve data security.
↙ Reduce Power Consumption (ESG)
↙ Reduce Cost (Bandwidth, storage, Cloud Computing)
↙ Reduce Lantency (only 50 ms to complete recognition)
Cutting-edge model development, powered by the Qualcomm AI accelerator, delivers efficient, low-power multi-lane and multi-object detection, processing over 50 images per second for real-time accuracy and timely decision-making.
Boasting an impressive 96%+ accuracy rate, the model is trained on millions of image data points, encompassing both simulated and real-world scenarios. This ensures precise identification even in challenging conditions like nighttime or complex road environments.
Deep learning, motion vector prediction, edge detection, and other advanced techniques are employed to enhance the speed and accuracy of lane detection analysis. This adaptability makes the system suitable for diverse environments, from well-marked highways to winding rural roads.
The model is constantly optimized and refined, staying at the forefront of lane detection technology. It empowers various safety features like lane departure warnings, active lane-keeping assistance, forward distance warnings, event notifications, and warning point prompts, enhancing driver safety and confidence.
Cutting-edge model development, powered by the Qualcomm AI accelerator, delivers efficient, low-power multi-lane and multi-object detection, processing over 50 images per second for real-time accuracy and timely decision-making.
Boasting an impressive 96%+ accuracy rate, the model is trained on millions of image data points, encompassing both simulated and real-world scenarios. This ensures precise identification even in challenging conditions like nighttime or complex road environments.
Deep learning, motion vector prediction, edge detection, and other advanced techniques are employed to enhance the speed and accuracy of lane detection analysis. This adaptability makes the system suitable for diverse environments, from well-marked highways to winding rural roads.
The model is constantly optimized and refined, staying at the forefront of lane detection technology. It empowers various safety features like lane departure warnings, active lane-keeping assistance, forward distance warnings, event notifications, and warning point prompts, enhancing driver safety and confidence.
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.