Team B4: Smart Library

There is still not much that I could have done this week, before we get the data from Sorell’s. However, I did manage to find some useful resources. Specifically, I found a website called Roboflow, which will allow me to take my labelled training data and run some preprocessing on them. This is different from the preprocessing that we plan on running in the central node, as it specifically pertains to the machine learning model.

The main advantage that Roboflow offers is that it will help me convert images from XML to the darknet format in bulk. For the initial picture of my workspace that I used to test the pipeline, I did this manually. Now that I have found Roboflow, I can do this automatically, saving a lot of time to process thousands of images.

Another advantage that Roboflow offers is increasing the size of my dataset. It lets me perform transformations like rotation, scaling and blurring on duplicates of the images in the dataset. With combinations of these transformations I could increase the size of my dataset by a factor of 3-10. One consideration I will need to make is that this compromises on the quality of the dataset, since the images will have some similarity among themselves.

On a different note, one issue that Pablo brought to my attention is where some of the images taken are distorted. The bottom part of the image is cut off and replaced with vertical lines, as shown in this picture. Pablo mentioned this was due to a wiring issue, but I am also planning on solving this problem in software.

One thing to note is that the lines that cause the distortion are all perfectly vertical and they are always at the bottom of the picture. This can be detected using a vertical Sobel filter. The Sobel filter is a high pass filter for images in one dimension. Since there is no change vertically in the bad part of the image, there is only a DC bias. A high pass filter will remove this bias and leave the bottom half of the image to be all zeros. After that, we simply need to compare the last bottom lines of the image to zeros in order to detect this kind of error.

I was not able to do much work this week on the project, due to my other commitments. Next week, we should have some data available, so that I can start training the machine learning model.

There was not much work for me to do this week, since the machine learning data is not yet available. I spent most of my time on getting familiar with AWS and reflecting on the ethics readings.

For AWS, I read up on the S3 product. S3 stands for Simple Storage Service, and we may use it to securely maintain and access our data on the cloud. It has multiple tiers like Standard, Infrequent Access and Glacier for varying levels of access amounts and latency requirements. After reading up on all of them, I will use the Standard S3 bucket. It has a durability of 99.999999999% and availability of 99.99%. The high durability ensures that the data will not be lost and the high availability will ensure good enough latency for the purpose of training a machine learning model. Other than S3, I also plan to use EC2 for this project, but I had researched EC2 before this week and my research into it this week did not reveal any new information that changed my plans.

I also spent a good amount of time this week on the ethics readings. I found Langdon Winner’s paper particularly interesting. I didn’t know the extent to which simple design choices made by engineers affected society and culture. It has made me more mindful of my project and gotten me to think about unintended consequences that may arise. While working with data containing images of real people, I must be very careful that the data is secure and used only for the purposes of this project. Otherwise, it could be misused and violate people’s privacy.