Research on Video Real-Time Analysis and Recognition Algorithm Suitable for Transmission Line Corridor Environment

The video real-time analysis and recognition is critical in environment suitable for transmission line corridors, however it has an issue with erroneous performance positioning. The typical Deep learning algorithms is unable to address the learning algorithms issue in environment suitable for transmission line corridors, and the result is insufficient. As a result, a Bee colony algorithm-based research on the video real-time analysis and recognition algorithm suitable for the transmission line corridor environment is provided, and research on the video real-time analysis and recognition algorithm suitable for the transmission line corridor environment is assessed. To begin, the swarm behavior theory is used to discover the influencing elements, and the indicators are split based on the video real-time analysis and recognition's needs to decrease interference factors in the video real-time analysis and recognition. The swarm behavior theory is then used to create a Bee colony algorithm video real-time analysis and recognition scheme, and the outcomes of the video real-time analysis and recognition are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Bee colony algorithm outperforms the standard Deep learning algorithms in terms of video real-time analysis and recognition accuracy and time of influencing variables.