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The Most Advanced Deep Learning Algorithm

The high accuracy of SVNet can be achieved by reflecting a variety of state-of-the-art techniques in learning algorithms, rather than simple learning, such as meta-learning-based data sampling, feature-enhancing learning, hard example mining, and knowledge distillation. This allows SVNet to find as many target candidates as possible and refine them to deliver higher performance.

Object Detection

Lane Detection

Traffic Sign Recognition / Traffic Light Recognition

Light Source Detection

Low tier SoC (Less than 4TOPS) OD, LBF(Lane, Road boundary, Freespace), TSR, TLR, LSD, DE 30fps SoC Function Frameper second

High Efficiency

SVNet is a lightweight solution using compact DNN-based algorithms that can minimize the amount of computation required per frame. This helps maximize efficiency by minimizing memory and power consumption required for computation.
This allows our customers to maintain strong performance over 30 fps even on low-cost chipsets less than 4TOPS.

Low tier SoC (Less than 4TOPS) OD, LBF (Lane, Road boundary, Freespace), TSR, TLR, LSD, DE 30fps SoC Function Frameper second

Fully Optimized Modular Solution

SVNet can be fully optimized and deployed onto any hardware customer needs for their ADAS and self-driving car. And this will bring customers time to market benefit

MODULAR SOLUTION
YourHardware OurSoftware Fully OptimizedADAS Unit
Single Image

Flexible Solution to Support
Any SoC

Taking advantage of its lightweight capabilities, SVNet has already been applied to more than 18 platforms for hardware-optimized performance, fully running on commercially available platforms as well as those in development stages.