This week I continued working on the individual component classification testing and the full circuit classification testing. The dataset is now complete and finalized representing 54 component subimages. I also factored in each orientation (left, right, down, up-facing) of voltage sources, current sources, and diodes into the code. To do this I had to not use the rBRIEF (rotated BRIEF) descriptors that ORB uses, but just the standard ORB descriptors. Unfortunately this required me to use the opencv-contrib extra modules, which required me to build the opencv library from source with the extra modules included, which took like 10-15 hours. In the meantime I used my python code for the testing. The good news is that the new component classification measurements are now ~94% on correct component classification, and ~79% for the correct orientation, which is now a raw component classification score of ~85%. It is not exactly the >=90% we originally outlined, but I am pretty happy about it. See the below graphs for some comparisons I made.
I also started doing the circuit classification testing. As of now I have only done 12 tests and 9 of them were correctly classified. Oddly enough what was the root of the problem in the three tests I failed was that switches could not be classified properly, which shocked me because in the individual component classification testing they were the most accurate by far. I am going to continue looking into how to solve it. It doesn’t seem like an issue with my individual classification testing because when I added the switch subimages generated from the circuit classification testing, they also failed, so I guess it just turns out that the ones used in the individual testing set were similar to those in the dataset.
For next week I am going to continue the full circuit testing and make some deliverables (graphs, tables) to show the result of the testing. I will also look into the switch thing and further classification improvements as I see fit. I am decently satisfied with the state of the computer vision and believe it is decently ready for final demonstration.