Joseph:
This week I worked on creating the presentation for the design review as well as the design document that followed it. I also began looking for different Faster-RCNN repositories on Github as we decided that reimplementing the entire model ourselves would be too tedious of a task and we would not gain much from that experience. Our main task is to have an existing model to tune and bias. I was able to come across a model that seemed promising as it provided the option to run on both gpu and cpu, which is exactly what we need.
For next week, I would like to have this model running and then I will begin helping Karen with obstacle avoidance when the sensor come in.
Karen:
This week I focused on the design review and putting together the official write up for this. After the presentation, we reviewed the immediate feedback and have tried to incorporate this into our future plans. I am currently trying to come up with more benchmarks for the path planning and obstacle avoidance part of the project. This week we also just received the SD card and reader, so now we can finally upload to the pi. The time it took for this to arrive has caused a significant amount of delay and we hope to address that by putting in more time outside of class. I have downloaded the Raspbian image onto my laptop and have started setting up the pi for use.
Hopefully, now that we ownership of all the different parts, we can finally start getting code uploaded onto the pi and have it running on the roomba. I do think that there might need to be some adjustments with how the commands are serially sent, so I will have to add some additional testing to see if they are being received properly. Currently, the networking protocol I have implemented is based off of example scripts. However, I think we might need adjustments, regarding time and frequency. So, once the code is uploaded, I will have to adjust for that. Overall, I think there has been overestimation in how quickly we would be able to get the Roomba to move. So ideally I would like to have successful movement by the end of next week- assuming all class time will be used for working.
Manini
This week I focused on the design review and my presentation. Joseph, Karen and I had to fine tune some design specifications including metrics and implementation decisions. During our design review, the Professor suggested that it might be a good idea to try using both YOLO and Faster RCNN and compare the performance of both. Thus, for the later part of the week I began searching for a good YOLO implementation and learning more about the internals of the model. In terms of Faster RCNN, I downloaded one version on my laptop and am currently trying to get it up and running with Pytorch. I am also currently trying to sift through the COCO dataset and limiting the size of the data we will be using. This was another suggestion by Professor Savvides to improve the training time for our model and to stay on track.
In this upcoming week I would like to begin training our Faster RCNN model and get some baseline performance metrics so we can plan for how we want to bias/ fine tune the Faster RCNN model. The design review took up a little bit more time than we had anticipated so this week we plan on putting in extra hours outside of class to catch up and finish our first sprint before spring break.
TEAM STATUS:
- What are the most significant risks that could jeopardize the success of the project? How are these risks being managed? What contingency plans are ready?
The biggest risk right now is still staying on schedule. We did not account for the time it takes to make presentations and reports which has really limited the amount of time we have to do other things.
- Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
One change that we have made is that we will also be trying to use the YOLO model for human detection. This change is so that we can compare the performance of both Faster RCNN and YOLO to achieve the best combined accuracy and inference time.
C9