This week I planned on developing the machine learning architecture. I started by loading up our chosen dataset and examining its structure. Upon this analysis, I found several confusing points that weren’t answered in the dataset’s readme file. I then emailed the authors of the paper “Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach” (the paper on which were basing our approach). I’ve since been corresponding with those authors. They pointed out that the dataset didn’t include range-doppler signatures, which we absolutely have to train on to successfully detect moving humans. They pointed me to another dataset which is must more suitable. The authors also attached 4 extremely helpful research papers that spoke more about drones and using mmWave radar to detect humans. I read and reviewed each of those. I learned two very important things. Due to the low resolution of radar data, it will be necessary to construct range-doppler maps to gain more information and achieve higher resolution, which will both in turn aid in detecting humans. Next, although I knew I wanted to implement a CNN architecture, one of the papers–“Radar-camera Fusion for Road Target Classification”–pointed out a high performing CNN architecture that I would like to implement.
Because I spent this week understanding and laying out the architecture, I wasn’t able to get to implementation. This puts me a week behind schedule. However, I think this time was very well spent, because my progress going forward now feels a lot more structured. Although I’m behind schedule, I think by implementing the network this week, our progress will still be on track.
By the end of this week, I would like to get the network training. Since I am using a CNN architecture on high dimensional training data, this process will take a long time, including tuning hyperparameters. Therefore, it’s very important I get this started early.
Before this project, I had only been exposed to radar briefly in 18-220. To learn more about radar and its collected data, I read the 4 research papers recommended by the authors of the aforementioned paper. For the ML architecture, I am a machine learning minor and have implemented my own CNN’s in Intermediate Deep Learning (10-417).
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