针对目前传统人工巡检效率低、存在视野盲区、成本较高等问题，设计了一种基于DiMP算法的安防巡检系统。该系统采用模块化设计，在嵌入式机载计算机上实现了无人机的自主飞行控制及跟踪功能。为提升巡检过程中对小型目标的跟踪精度与准确性，采用多尺度特征融合策略对 DiMP目标跟踪算法进行改进，将不同尺度的图像金字塔特征与骨干网络特征进行融合，为骨干网络提供信息丰富的融合特征。优化后的DiMP算法在UAV123数据集上的目标跟踪成功率和精度分别提高了2.6%和3.4%，同时在VOT2018数据集上实现了38 fps的跟踪速度。最后，在室外环境验证无人机安防巡检的效果。结果表明，改进的目标跟踪算法能够在无人机上实时运行，并能够对目标进行长时间且稳定的跟踪。

To address the issues of low efficiency， blind spots in vision， and high costs of traditional manual security patrols， a security patrol system based on the DiMP （discriminative model prediction） algorithm was designed. The system adopted a modular design and implements autonomous flight control and tracking functions for UAV （unmanned aerial vehicle） on an embedded onboard computer. To enhance the tracking precision and accuracy of small targets during the patrol process， a multi-scale feature fusion strategy was employed to improve the DiMP target tracking algorithm. This strategy involved fusing image pyramid features of different scales with the backbone network features， providing the backbone network with information-rich fused features. The optimized DiMP algorithm achieved a 2.6% increase in target tracking success rate and a 3.4% increase in precision on the UAV123 dataset， while also reaching a tracking speed of 38 fps on the VOT2018 dataset. Finally， the effectiveness of the UAV security patrol was verified in an outdoor environment. The results show that the improved tracking algorithm is capable of operating in real time on the UAV and stably tracking the target for a long time.