Author: lctummal

We have tested all of the metrics, and we are in the process of developing the survey. This only requires the full functionality of the web application, so realistically, we could perform the study with just that part functional, but we intend on finishing in the next few days, so having users work with the full product would be nice.

As for unit tests, we have latency measurements for the camera, card detection system, and the processing algorithm. We also have a latency measurement for the full system in total for the total system latency spec of 1s. We have accuracy tests for the card detection and processing algorithm. We intend to perform the survey as well, but do not have results at this time.

I have worked on the poster for the demo and am working on parts of the final report that can be completed prior to the final construction of our product. I have also hot-glued the planks back on, so hopefully, we do not have to worry about the box tipping over again, though even if it does, we can just place the planks back on, this time using tape or a stronger adhesive. In the final days before integration, I may paint the box to give a more visually appealing design, and also to remove any wooden dust off the box.

As a team, we are attempting to work through two main tasks. The first is the integration of the web app and the ML model. As mentioned in individual reports, the web app can now receive data from a local source, so now we are trying to test this process in a real-time system. There are some small issues to consider, such as duplicate cards (which can happen with multiple decks), not double counting cards, etc. We hope to finalize this process by next week, but for now, we can start the second main task, which is testing the metrics. For the presentation, we hope to get most of these metrics done, but for some metrics, such as the user survey, we won’t have results immediately. We have finalized our methodology, and very soon, we will conduct these tests.

I was able to address the changes mentioned in the last status report. I laser cut two long planks and glued them to the bottom of the box, with each of them protruding out in the same direction, so that if the camera were to stick out in that direction, the box would no longer tip over. Some of the planks fell apart, so I plan to hot glue them soon. Also, to further stabilize the camera mount, I laser cut a plank with a hole in it. The camera mounting hole almost perfectly sticks through it, so once I hot glue that to the top, the camera mount should exhibit very limited movement when the user moves the camera. On top of this, I have been working on the slides for the final presentation.

We were able to run the ML model on the Jetson Nano at last. We initially thought that this would entail reflashing the microSD card, but it was actually possible with just a few Python installations to get to 3.11. Now, this leaves the final major integration step, which is the transmission of card detections from the ML model to the processing algorithm, which we are working on. We also would like to broadcast the camera feed to the web app so that the user can calibrate the camera. Once we finish these integration steps, we then will start testing the key parameters of our product to assess viability.

Since the last status report and demo, a lot of work has been done on improving the apparatus that houses the Nano and the camera mount. This improved design is made of plywood and stuck together with hot glue. After piecing the structure together, I tested the integrity of the structure, and it seems to be good, except for the case where the camera significantly tilts over the edge, at which point the structure falls. The solution here, which I will implement shortly, is to glue the box to long flat planks that jut out forward. In this case, even if the camera leans forward, the box won’t fall forward.

This week, I have received both the camera mount and the camera. Later today, I will test the functionalities of both and see if I can integrate it smoothly with the Jetson. Much of our integration steps for the interim demo will take place tomorrow. Also, I have laser cut the parts for the box that will house our camera mount and jetson. I don’t have a picture right now since I’m on campus, but I can update this post at some point with a picture. This is a basic prototype model that was made out of cardboard since scrap wood was not available, though for our final version I will use wood. The dimensions are perfect to fit our Jetson, and the camera mount wire is actually very long, so I imagine I can wrap it inside the box and then attach the camera. Once these pieces go together, we will have a very basic working implementation.

This week, I have finished most of the model for the product that will house the camera and the jetson device. Pictures are attached. Each face has a bit of a jigsaw pattern so that a preliminary assembly can be done for prototyping without actually using a permanent solution like hot glue or screws. I intend to use wood to laser cut the material, so once I learn to how access wood, I will do so. The jetson will fit in the bottom compartment (almost perfectly), and the camera mount will stick out of the top. There will be a small platform inside the box that will act as the mount for the camera mount to attach to, so once I pick up the camera mount on Monday, I can configure how that will look. The holes in the box back side are for ventilation. I’m not sure if that would even be a concern, but it’s good to make sure, in case we happen to close the top for whatever reason. Wires will run from the bottom opening (where the jetson would be inserted), and this consolidation will make the product usage much easier. I may design a quick way to keep the jetson in place during operation.

This week, I have been getting back on track on my tasks after spring break week. Finally, we have settled on the gooseneck camera mount, which is the most effective way to stick the camera and then adjust it. The previous idea of a lid is very error prone and involves a lot more iteration, since it relies heavily on the materials used for the hinge as well as the design. The gooseneck mount should work better. This means that we need a platform inside the box to attach the mount to. Also, to attach the box, I plan to use a jigsaw structure, as this will support the structure best, and gives a degree of portability in case the user wants to disassemble and carry it to places. By either next week or the one after, I should have a printed prototype of the box.

After items were picked up, I wanted to set up the camera and Jetson together to make sure they work, since initializing devices can often have compatibility problems. The first step was to flash the SD card with the image for the Linux operating system. Then, I inserted the microSD into the Jetson and connected the Jetson to a monitor. There were several issues with setup on the first attempt, related to running “sudo apt-get update.” This required me to flash the microSD, and to not update. I downloaded a demo program with “DepthAI” (which we likely will not use on the first attempt of writing code), and connected the camera. The result was a (somewhat) functional object detection. Currently, I am on track, since I anticipated setup to take more debugging efforts. Now, we can begin to run some smaller sample computer vision programs to determine the accuracy of card detection. I can also start profiling different methods of card detection, such as machine learning (which can usually guarantee a higher accuracy) and computer vision (which can be simpler but with no guarantee of accuracy).