This was my first week after onboarding and being familiarized with the project. My task was to evaluate the existing deep-learning implementation of court-detection, ball detection, and player detection. I started by analyzing the existing results and highlighting all false positives and false negatives. After concluding that court detection was the most critical model to perfect (as it impacts all downstream detection models), I conducted a deep dive investigation of the court detection architecture and code. The court detection model uses both traditional computer vision and deep learning methods to detect all lines on the court. I found an issue in one of the traditional computer vision elements (the black/white threshold after graying the image), which causes false negatives. After meeting with the team and discussing these findings, we selected this as the first code section to be improved.