  The CoachAI Badminton 2023 Track1 initiative aim to automatically detect
events within badminton match videos. Detecting small objects, especially the
shuttlecock, is of quite importance and demands high precision within the
challenge. Such detection is crucial for tasks like hit count, hitting time,
and hitting location. However, even after revising the well-regarded
shuttlecock detecting model, TrackNet, our object detection models still fall
short of the desired accuracy. To address this issue, we've implemented various
deep learning methods to tackle the problems arising from noisy detectied data,
leveraging diverse data types to improve precision. In this report, we detail
the detection model modifications we've made and our approach to the 11 tasks.
Notably, our system garnered a score of 0.78 out of 1.0 in the challenge. We
have released our source code in Github
https://github.com/jean50621/Badminton_Challenge
