Deep Learning for Traffic Counting
Did you know that traffic counting is behind the rise of Microsoft and thus modern personal computing? Traffic counting is used by city officials to determine transportation needs. The most popular method is by laying a strip of pneumatic rubber tube on the roadway of interest and analyze metrics like vehicle volume, vehicle types, axle counts, etc. This method, however, is not quite accurate or safe so a better method is researched. In this article, you will find how you can build your own traffic counter using a Deep Learning approach (YOLO V5) using a public dataset from the ground up. The entire source code is available in the Colab notebook.
Problems
Interestingly, before they founded Microsoft, Bill Gates and Paul Allen first tried their hands on automating some manual processes in punching in the pneumatic road tube data. The business partnership was named as Traf-O-Data which later became Microsoft. While the project itself was not very successful on its own, the experience they gained eventually led to founding Microsoft and the rest is history.
Though pneumatic rubber tube counters are easy and widely used solutions, there are a few problems:
- Setup risks: To set up the tubes, someone has to go out in the middle of a high-traffic roadway. This is risky and poses an insignificant amount of…
