Renesas and StradVision have jointly developed a deep learning-based object recognition solution. The solution will find application in smart cameras used in next-generation advanced driver assistance system (ADAS) applications and in cameras for ADAS Level 2 and above.
Renesas delivers embedded design innovation with semiconductor solutions that enable connected, intelligent devices while StradVision is a vision processing technology company, that provides the underpinning that allows ADAS in Autonomous Vehicles to reach the next level of safety, accuracy and driver convenience.
To avoid hazards in urban areas, next-generation ADAS implementations require high-precision object recognition capable of detecting vulnerable road users (VRUs) such as pedestrians and cyclists.
The Key features of the deep learning-based object recognition solution:
1. Solution supports early evaluation to mass production
StradVision’s SVNet deep learning software is a powerful AI perception solution for the mass production of ADAS systems. It is highly regarded for its recognition precision in low-light environments and its ability to deal with occlusion when objects are partially hidden by other objects. The basic software package for the R-Car V3H performs simultaneous vehicles, person and lane recognition, processing the image data at a rate of 25 frames per second, enabling swift evaluation and POC development. Using these capabilities as a basis, if developers wish to customize the software with the addition of signs, markings and other objects as recognition targets, StradVision provides support for deep learning-based object recognition covering all the steps from training through the embedding of software for mass-produced vehicles.
2. R-Car V3H and R-Car V3M SoCs increase reliability for smart camera systems while reducing cost
In addition to the CNN-IP dedicated deep learning module, the Renesas R-Car V3H and R-Car V3M feature the IMP-X5 image recognition engine. Combining deep learning-based complex object recognition and highly verifiable image recognition processing with man-made rules allows designers to build a robust system. In addition, the on-chip image signal processor (ISP) is designed to convert sensor signals for image rendering and recognition processing. This makes it possible to configure a system using inexpensive cameras without built-in ISPs, reducing the overall bill-of-materials (BOM) cost.
Renesas R-Car SoCs featuring the new joint deep learning solution, including software and development support from StradVision, are scheduled to be available to developers by early 2020.
Source: Press Release