Artificial Intelligence is an interesting field that has become more and more integral to our daily lives. Its applications can be seen from facial recognition, recommendation systems, and automated client services. Such tasks make life a lot simpler, but are quite complex in and of themselves. Nonetheless, these tasks rely on machine learning, which is how computers develop ways to recognize patterns. These patterns require loads of data however so that computers can be accurate. Luckily in this day and age, there is a variety of data to train from and a variety of problems to tackle.
For this project, I wanted to use AI to show how it can integrate with other innovative technologies in the lab. I went with object detection because its applications in engineering rely on micro-controllers, data analytics, and the internet of things. For example, a self driving car needs to be able to tell where the road is or if a person is up ahead. A task such as this simply wouldn’t function well without each component. Looking into the future, we will need more innovative ways to identify things whether it be for a car or for security surveillance.
When I first started working in the Innovate Lab, I saw the LCD plate in one of the cabinet drawers and wanted to know how it worked. I was fascinated with its potential and decided to focus my project around the plate. The project itself was to have a Raspberry Pi recognize an object and output the label onto an LCD plate. The first aspect, recognizing objects, wasn’t too hard to create. Online there are multiple models that are already trained for public use. These pre-trained models tell the computer the algorithm to recognize a certain object. The challenge came with the installation of all the programs required to use the model and to run the script. Afterwards, outputting the text onto the plate simply required me to wire the plate to the pi. The need for the plate was to show the results otherwise they’d have to plug in a monitor, but other alternatives could have been used as well. For example, the results could have been sent online to a web application or they could have been stored in a file on the pi. So far the results haven’t been always accurate, but that just leaves room for improvement. I am hoping that soon I will be able to run the detection script on data that has been streamed to the pi. Overall, I gained a better understanding of the applications of AI and engineering. This was only one of the many capabilities of AI and there is still so much more to try.
By: Robert McClardy