How to quickly prototype 4x machine vision applications on one small embedded system.
Embedded vision components are ever-popular and are being incorporated into a plethora of applications. What all these applications have in common is the need to pack more and more functionality into tight spaces. Often, it is also very advantageous for these systems make decisions on the edge. To enable such systems, including the ability to prototype quickly, Teledyne FLIR has introduced the Quartet™ Embedded Solution for TX2. This customized carrier board enables easy integration of up to 4 x USB3 machine vision cameras at full bandwidth. It includes the NVidia Jetson deep learning hardware accelerator and comes pre-integrated with Teledyne FLIR’s Spinnaker® SDK. Often, it is also very advantageous for these systems to make decisions on the edge, especially in inspection, mobile robotics, traffic systems, and various types of unmanned vehicles.
To highlight what the Quartet with Spinnaker SDK pre-installed can enable, we describe steps taken in developing an ITS (traffic systems) inspired prototype running four simultaneous applications, three of which use deep learning for:
- Application 1: License plate recognition using deep learning
- Application 2: Vehicle type categorization using deep learning
- Application 3: Vehicle color classification using deep learning
- Application 4: See through windshield (past reflection & glare)
Read this very practical article which includes (1) a shopping list, (2) development time for each application, (3) number of training images required, and finally, actions to optimize overall system performance.
