Hi, I’m new to the deep learning space and wanted help creating a workflow for realtime defect detection in an industrial setting(focused on PCB defects). Optimally, I would be able to train the model using GPUs on AWS and deploy on an edge device like the Jetson. I have two ideas for an approach:
Treat the defect detection like an object detection problem. Train a TLT object detection model on a defect dataset in AWS and export the model for deployment on Jetson with Deepstream.
I found this paper treating defect detection as an image segmentation problem: https://nvidia-gpugenius.highspot.com/viewer/5c949687a2e3a90445b8431f. To summarize, the approach is to create a Unet model in TensorFlow and run inference with TF-TRT.
Overall, my questions are:
- Is one of the approaches more viable than the other?
- How would I adapt the second approach for realtime defect detection? Could I do something similar to the first approach to deploy the model on a Jetson?
I am new to this process, so examples/implementation details are more than welcome!