This week, I mostly spent time making the sensor platform ready to be attached to the exterior box. I helped laser cut the wood pieces for the sensor platform using Tate’s CAD design. Each of the sensor wires was too long (multiple feet), so I cut them all to around 8-10″ in length and re-soldered all of the wires to connect them to stronger, additional wires so they could actually be inserted into a breadboard properly (the original wires are too flimsy). I also calibrated all of the capacitive sensors to the materials we want to detect (HDPE plastics and glass) for each of the upper and lower bounds (3 per bound). When calibrating, I determined that the capacitive sensors sensitivity was unable to be adjusted enough to recognize PET plastics, so the sensors ultimately will only be able to detect HDPE plastics. This may narrow our scope more on plastic detection, but I am hoping the image classifier may be able to recognize the more common items that are made of PET plastics, like plastic water bottles. I also tried to follow a tutorial for training our ResNet model, but ran into some issues running the code on AWS.
I am slightly behind in my progress since I didn’t get the training done on AWS, but I’ll be trying more training tutorials meant to run on AWS this weekend.
Next week, I hope to be done with the AWS training for the image classifier or alternatively, finding a more accurate algorithm to train on the Nano instead (since Jessica already trained on the Nano, but results were not too accurate). I also want to do object detection and background subtraction on images received by the Raspberry Pi camera, since the camera was able to be connected to the Nano this week.