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 major risks remain the same as last week: the weight of the device, the PCB connection between Jetson and peripherals, and the identification of partial frames of objects.
A new risk that can potentially jeopardize the success of the project is the dependency on the object recognition model. We have realized that training the Yolov4 model with our own dataset is no longer possible due to the malfunction in darknet, which is the responsible team that has supported the recognition model. Therefore, we have changed our plan to upgrade the model to Yolov5, which is more recent than Yolov4 and is implemented by a more reliable team Ultralytics. The risk of such dependency can be mitigated by upgrading the version one by one as time permits. Our reach goal is to upgrade to Yolov7, which is relatively new, and attach a distance estimation module to the new version.
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?
No major changes have been made to our design. Using the suggestions from the LAMP advisory board, we are focusing on datasets for the OR model incorporating hallways, stairs, doors, trash cans, and/or pets, since these are obstacles they identified as common and necessary to identify. One change of a design of the system is that the OR model has changed from Yolov4 to Yolov5 due to the outdated dependency and unsupported module.
Provide an updated schedule if changes have occurred.
Because the OR model has been upgraded to version 5, it needs a new distance estimation feature to be integrated. Therefore, we have postponed testing the image recognition model by a few days and added some time to work on integrating the feature to the upgraded model. 