This past week I focused on implementing and testing various neural network-based object detection architectures and working on the design documentation. My goals this week were to pull up a fully functional Faster R-CNN model to test with, have the rough CV pipeline laid out and study Open CV further.
The bulk of my time was spent pulling up the models and performing preliminary testing. In my research, I found a promising Object Detection library – Facebook Detectron 2 with support for various Faster R-CNN architectures. Briefly, Mask R-CNN became a consideration since the object masks could help with object occlusion for our use case (ie table covering chairs), but I ultimately decided against it as Faster R-CNN would work sufficiently and the object masks would add significant overhead in labeling training data.
I tested a number of Faster R-CNN architectures on some test images we took after class Monday. Ultimately, I found the larger Faster R-CNN – Reset 101 architectures had higher accuracy and were able to detect more objects. During this testing, I tested out Ultralytics Yolov5. Yolov5 surprisingly performed similarly to the larger Faster R-CNN architectures despite the smaller model size and faster computation time. For this reason I decided on working with the Yolov5 instead of the Faster R-CNN.
From there, I spent some time determining the rough overall CV pipeline – discussing with Chen how to translate the object detection output into the seat occupation data. I added this final pipeline to the Design Review powerpoint slides.
I didn’t research as much about Open CV – I fell sick during the week so I lost some time that I might have used to research Open CV. Based on what I was finding with model testing though, I’ve found I mainly need to look into noise reduction, contrast increase and some potential image segmentation for preprocessing.
Next week, I’ll research the Open CV needed for the image preprocessing layers to catch up in that area and will start putting together the image preprocessing with the model. Besides that, next week’s goals include deciding what training data to collect and starting to collect training data using the camera setup.