Computer Vision AI models for Additive Manufacturing image processing

Overview

The work will entail:

The NIST Information Technology Lab (ITL) and Engineering Lab (EL) are collaborating on a project for real-time image processing for Additive Manufacturing. To handle real-time constraints, computations on Field Programmable Gate Array (FPGA) devices will need to be enabled, likely involving both traditional Computer Vision algorithms and Deep Learning models.

We plan on instrumenting a hard real-time system that can meet the time sensitive deadlines for detecting sparks from a high-speed camera that is monitoring the interaction between the melt pool and laser. There are three methodologies to consider.

1. The camera contains a built-in FPGA that can process images as they are captured.

2. The capture card has a slightly higher-end FPGA.

3. The capture card can transfer image data into system memory, allowing the host system to process images using either the CPU, GPU, or a combination of both.

To this end, we are seeking a senior Computer Scientist who will supervise development of algorithms to process frames in real-time from a high frame rate camera. The processing algorithms may utilize the camera’s built-in Field Programmable Gate Arrays (FPGA), the capture card’s built-in FPGA, or traditional computer CPUs and GPUs.

Note that the exact number of hours per week will be negotiated once the successful candidate has been identified.

Qualifications

• A PhD in Computer Science, Engineering, Manufacturing, or a related field.
• At least 10 years of relevant experience supervising graduate students, or equivalent.
• Familiarity with image analysis algorithms.
• Familiarity with FPGA programming.
• Familiarity with CPU and/or GPU image analysis.

Research Proposal

Key responsibilities will include but are not limited to:

• Supervise development of image analysis algorithms that target the highspeed camera’s FPGA.
• Supervise development of image analysis algorithms that target the capture card’s FPGA.
• Supervise development of image analysis algorithms that target the traditional computer’s CPU(s) and GPU(s).
• Supervise measurement of real-time throughput for developed image analysis workflows.
• Supervise creation of AI/Deep learning workflows for training AI models for analyzing images in a series.