The Jetson Nano™ module is a compact 70 mm x 45 mm embedded processor module based on a Tegra processor you’d expect to find in the data center. The system on chip at the heart of the board contains a Maxwell architecture GPU with 128 CUDA cores alongside a quad-core Arm Cortex-A57. This equates to 472 GFLOPS of total performance at the expense of only 5 to 10W power consumption.
The Nano SoC also integrates 720p, 1080p, and 4K HEVC video encoders and decoders that deliver 250 MP/s and 500 MP/s of performance, respectively. These are, understandably, key components in an ALPR application that has to apply neural networking algorithms to stream video in real time.
In addition, Nano supports 4 GB of 1600 MHz LPDDR4 memory for the fast, frequent memory accesses required by deep learning applications, as well as 16 GB additional eMMC storage. Interfaces such as GbE, HDMI 2.0, eDP, and USB 3.0 are brought out through a companion carrier board.
The low power, low cost Jetson Nano provided a solid foundation for SmartCow’s deep learning-based ALPR solution, Sentinel.
(Figure 1) The NVIDIA® Jetson Nano™ is a small form factor embedded processor module designed for computer vision and deep learning applications.
To offset the problem of re-registering images of the same license plate, SmartCow developed a feature called similarity search. Similarity search registers first time a license plate is detected and discards all duplicate images until the vehicle leaves the frame, saving valuable memory space.
In addition to storage capacity to save more than 2.5 million images directly on the device, Sentinel also supports low-cost 4G connectivity modules that allow metadata to be streamed back to transit authorities.
All of this at only 10W of power.