In this week, our team puts significant effort on consolidating our ideas into a 14-page paper, listing everything that that we have researched on and will accomplish for our project.
The most significant risk would be if the Yolo implementation has lower accuracy than we expected such that it would not reach our design requirement. However, even if this happens, the risk can be managed by training of the network, ruling out edge cases, and tuning. At least three other contingent plans are listed in section 5 of our paper and edge detection method will be the next algorithm we will pursue if the Yolo method does not work.
Quite a few changes are made to our design in the algorithm we use for object recognition. This week, the team decided to pursue Yolo as the primary algorithm such that the SLAM ways are now ranked the third after edge detection algorithm. We believe that Yolo is simpler to implement without too much loss of accuracy and the change will have no costs going forward. By using the Yolo model, we could reduce the development time cycle and put more time into testing and tuning instead of building the model ourselves or constructing a SLAM.
There has been changes made to our schedule. The detailed schedule can be found as a part of the picture but also in the picture below:
