Shuttlecock Detection Using Residual Learning in U-Net Architecture

This paper introduces an enhanced approach for detecting shuttlecock. Detecting fast-moving objects, such as a shuttlecock, is crucial in various applications, including badminton video analysis and object tracking. Many deep-learning techniques have been proposed in literature to address this challenge. However, low image quality, motion blur, afterimage, and short-term occlusion can hinder accurate detection. To overcome these limitations, this research focuses on improving the existing method called TrackNetV2, which utilizes the U-Net architecture. The primary enhancement proposed in this study is incorporating residual learning within the U-Net architecture, emphasizing processing speed, prediction accuracy, and precision. Specifically, each U-Net layer is augmented with a residual layer, enhancing the network's overall performance. The results demonstrate that our proposed method outperforms the existing detection accuracy and reliability technique.