Research on Application of Multi-Target Tracking Algorithm Based on Improved Yolo in Target Tracking and Detection of Unmanned Storage Equipment

Due to the rapid advancement of industrial automation in recent years, unmanned storage equipment has found extensive applications in logistics and manufacturing. However, equipment tracking is still a key issue restricting production efficiency and safety. To tackle this challenge, the present study introduces an enhanced YOLO (You Only Look Once) based multi-target tracking algorithm. The algorithm aims to facilitate the tracking and detection of unmanned storage equipment. In this study, the basic principle and characteristics of YOLO algorithm are introduced first, and its efficient target detection ability is emphasized, and how to apply it to the field of multi-target tracking. Then, according to the characteristics of unmanned storage equipment, this study has improved on the basis of YOLO algorithm to adapt to the complex environment and changing target states. In addition to incorporating the spatial information of the target, the enhanced algorithm incorporates the historical motion information of the target. This integration enables more precise and stable target tracking by utilizing Kalman filtering and data cascade techniques. Extensive experiments are conducted in a real scenario of unmanned storage equipment to validate the effectiveness of the proposed algorithm. Experimental results show that the improved YOLO multi-target tracking algorithm can track multiple moving targets efficiently and accurately under complex background. This algorithm not only significantly improves the recognition and prediction ability of device tracking, but also achieves important results in providing more accurate information of device operating status and location. In summary, based on the improved YOLO multi-target tracking algorithm, this study has achieved remarkable application effects in the target tracking and detection of unmanned storage equipment. This algorithm can not only improve the reliability and operation security of the equipment, but also provide users with more intuitive and visual equipment operating status and location information, and provide strong support for equipment management and scheduling. In the future, this multi-target tracking algorithm based on improved YOLO is expected to be widely promoted and applied in the field of industrial automation.