Trajectory and landing point analysis of tennis based on improved real-time object detection algorithm

The high velocity of tennis balls presents significant challenges for visual trajectory perception and impact point localization, frequently leading to erroneous line calls. Although technological integration, particularly ball-tracking systems, has improved accuracy, these solutions are hindered by high equipment costs, dependence on manual calibration, and limitations in real-time processing capabilities. Deep learning-based approaches offer potential cost reductions but suffer from inherent detection latency and higher missed detection rates for small, fast-moving objects. The proposed multimodal fusion system integrates event cameras with conventional video streams. A spatio-temporal feature alignment module effectively addresses heterogeneous data synchronization and reduces the false detection rate caused by motion blur in high-speed videos by 58%. A Lightweight Hybrid Network (LHNet) is designed. Additionally, Dynamic Sparse Convolution (DSC) is employed to enhance computational efficiency for real-time operation. A Ballistic Trajectory Predictor (BTP) is introduced by incorporating aerodynamic drag and spin coefficients within a physics-constrained model, reducing the impact point prediction error to 1.2 cm - a significant improvement over the 3.5 cm error typical of conventional methods. Experimental validation on the TrackNet dataset demonstrates that the system achieves a detection accuracy of 98.7% (mAP@0.5), with a trajectory tracking ID-switch rate of only 0.5%, representing a 2.6-fold improvement over DeepSORT.