基于孪生网络车辆目标跟踪的物资仓储配送可视化监控方法

Visual Monitoring Method for Material Warehousing and Distribution Based on Twin Network Vehicle Target Tracking

随着物流行业的快速发展,仓储配送环境下的车辆目标跟踪面临复杂背景、遮挡、光照变化等问题。为解决这些问题,提出一种基于优化孪生网络的车辆目标跟踪方法。该方法在孪生网络的基础上引入注意力机制来增强提取目标特征的能力,采用改进的特征提取技术以适应复杂的仓储环境,结合Kalman滤波器提升跟踪的稳定性和平滑性。实验结果表明,本方法在各项性能指标上优于对比算法,在精确度、召回率和F1分数方面,分别达到94.2%、93.8%和94.0%的高水平。特别是在窄巷道、高架仓库等复杂场和极端条件下,该方法展现出优秀的跟踪性能和健壮性。

With the rapid development of the logistics industry,vehicle target tracking in warehousing and distribution environments faces problems such as complex backgrounds,occlusion,and lighting changes.To solve these problems,this paper proposes a vehicle target tracking method based on optimized twin networks.This method enhances the ability to extract target features by introducing attention mechanisms on the basis of twin networks,using improved feature extraction techniques to adapt to complex storage environments,and combining Kalman filters to improve tracking stability and smoothness.The experimental results show that this method outperforms the compared algorithms in various performance indicators.In terms of accuracy,recall,and F1 score,they reached high levels of 94.2%,93.8%,and 94.0%,respectively.Especially in complex fields and extreme conditions such as narrow tunnels and elevated warehouses,this method demonstrates excellent tracking performance and robustness.