February 2023

Team Status Report for 2/11

The most significant risks that could jeopardize the success of the project are related to integration. The first one will be gathering meaningful images to perform the ML algorithms on. As of now, we have acquired the radar and plan to begin image capturing within the next two weeks. With this comes the challenge of figuring out how to best position and use the radar so that the images it captures can be used with the ML algorithms we train. We will be using a dataset of standstill drone image captures to train our model, but until we begin image capture with the radar, the radar image quality is still a large risk that could delay the integration of the software with the hardware if the radar images are significantly different than the dataset images. These risks are being managed by starting the radar image capture as early as possible (i.e. within the next two weeks), since the ML training process will not be significantly far along before we start image capture. Therefore, we have allotted time to examine the radar captures together and ensure that they work with our dataset. In addition, we have looked into other radar image datasets and sources to find these datasets in case we find that our dataset is drastically different in comparison to our radar images.

One change we made to the existing design of the system is that we narrowed our project scope down to fire search and rescue missions. While we were planning on doing search and rescue missions that did not involve metal, since that would interfere with the radar, we did not explicitly narrow down the scope further than that. We received feedback from our TA that our use case scope was not extremely clear in our presentation and that it would be very helpful to do so in order to make clearer goals for ourselves and allow us to come up with a more specific testing plan. This change incurs no extra costs since it allows us to create more specific plans going forward and narrow down our needs. In addition to this change, we also added the creation of 3D chassis to encapsulate our device and have it rest on the drone legs. We had not previously included this in our project spec, but we needed to include something that would safely keep our entire device together and allow it to attach to any drone that could hold its weight. This did not incur many extra costs, since Angie has experience with 3D printing and was confident she would be able to create this chassis with ease. We had to allot one week to design and print this chassis to hold our radar and raspberry pi, which will occur once we acquire the raspberry pi, since we have already acquired the radar. This did not add extra time, since it can be done in parallel with many of the other tasks and does not have many dependencies.

As of now we are on schedule with our project, and plan to stay on track with our plans for the next few weeks.

Our project includes considerations for public health and safety concerns because of our use case. Our project is designed to help first responders stay safe by limiting the amount of time they are exposed in high danger areas. Our project also focuses on improving the efficiency and cost of search and rescue missions by using an mmWave radar. Currently the infrared sensor is more commonly used but can provide unclear results due to the flames. Since our radar would not get blocked by the waves from the fire, our project should allow for better human detection in fire, and thus help save more people.

By ayeshag, ago

Linsey’s Status Report for 2/11

This week I presented the project proposal for my group. As a group, we decided what information we wanted to convey in the presentation. Afterwards, I reviewed the slides and helped make the slides better for presenting–engaging and readable. During our meeting, Professor Fedder stressed that we must be able to answer “why” questions about our use case and requirements. To be prepared and effectively communicate our proposal, I researched current search and rescue drone applications and their standards, the drone market, and different radars–specifically our mmWave radar to gain more knowledge about it but also possible other choices for our device like lidar. After performing this research, I distilled what I learned into readable slides. I did my best to make our device easily imaginable by equating size with an iPhone 12, weight with half a bottle of water, and finishing our user requirements with a clear price comparison. Once the slides were polished, I practiced and timed out the presentation. Moving on, our progress is on schedule. In order to stay on track, I need to research ML algorithms to implement that would best achieve our goal of human detection classification. Therefore, I will perform this research and hopefully start training one architecture on our dataset, which we have already chosen.

By lszabo, ago

Ayesha’s Status Report for 2/11

This week I worked on finishing our project proposal slides for our class presentations. Specifically I worked on helping redefine our user and design requirements so that there was a clear distinction from the user and technical perspective. This included refining the metrics we had discussed and creating valid explanations behind each one. For the remainder of this week, I took into account some of the feedback we received from the questioning portion of the proposal presentation and looked into potential ways to address them. This included looking into temperature sensors and seeing how feasible it would be to include that aspect post-MVP. This also included refining our testing plan by thinking about what materials would give us the best results – I found that using varying thicknesses and opacities would allow us to best test our radar detection model. I also did a bit more research on fire search and rescue missions, after receiving feedback to narrow our scope down a bit. The research was mainly to see if we could get our device close enough to a fire, without having it melt or malfunction. From what we had previously researched, it seems as though we can get close enough to the fire to properly run our radar detection, but in edge cases where the obstructions require a smaller radius, our device would not be able to go closer to detect. Lastly, I started planning my own schedule for the next few weeks since the Gantt chart was quite large and I couldn’t break down all of my tasks to the checkpoints I wanted. I did not make a formal Gantt chart but I listed what I want to do/what that would require, and the deadlines I would like to finish those by.

My progress is on schedule. Next week I plan to look into buying a domain and setting up a general web app framework for our project. I also plan to work with my group mates on the design slides.

By ayeshag, ago

Introduction and Project Summary

Our project explores human detection and tracking through drone-based radar imagery. In emergency situations, humans are often hidden from view by fire, fog, and rock, eluding even the piercing gaze of night vision and thermal cameras. Enter radar, which can penetrate these obstructions for a better chance to detect and track stationary and moving humans in any weather condition. Owing to their maneuverability and low cost, drones make an excellent platform for first responders to safely inspect a search area at close range and return imagery from which humans can be automatically detected.

By angiebu, ago