Semantic-Based Motion Detection Method for Unmanned Aerial Vehicle Data Transmission

Unmanned Aerial Vehicles (UAVs) are crucial for wireless network transmissions, particularly in challenging en-vironments of regular inspection. However, transmitting high-resolution video data from UAV s poses challenges due to limited resources and significant data volumes. Traditional video compression methods, removing redundant information with a single frame, suffer from quality loss as compression rates increase. To address these issues, we propose a novel framework, namely Semantic-based Motion Detection Compression (SMDC) to perform the video compression with high-quality resolution. The proposed framework incorporates Generative Diffusion Change Detection (TransC-GD-CD), a robust semantic-based change detection method, to accurately detect motion between adjacent video frames. Specifically, frames with slight motion are eliminated, thereby reducing network bandwidth requirements for the UAV inspection. Furthermore, a neural network-based interpolation technique is integrated to restore the information loss and ensure smooth playback. Experimental results show that SMDC outperforms traditional compression methods based on H.264, achieving higher video quality at matched bitrates. The exceptional performance of SMDC promises its potential as an effective solution for high-resolution video transmission in scenarios with limited bandwidth.