A Multi-Target Tracking and Positioning Technology for UAV Based on Siamrpn Algorithm

Ligang Wu, Changxing Zhao, Zushan Ding, Xiao Zhang, Yiding Wang, Yang Li
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Abstract

UAV s play a pivotal role in the field of security due to their flexibility, high efficiency, and low cost. This article uses yolov4 convolutional neural network technology to achieve the target detection process of power inspection photos. First, use labelimg to accurately label the power inspection training data set, and then use the fusion target detection network yolov4 and the detection-based multi-target tracking algorithm DeepSORT to address the problem of UAV positioning the target. Use the SiamRPN algorithm to achieve high-precision positioning to meet the needs of power inspection services and quickly identify targets in batches.
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基于Siamrpn算法的无人机多目标跟踪定位技术
无人机以其灵活、高效、低成本的特点在安全领域发挥着举足轻重的作用。本文采用yolov4卷积神经网络技术实现对电力巡检照片的目标检测过程。首先利用标记技术对电力巡检训练数据集进行精确标记,然后利用融合目标检测网络yolov4和基于检测的多目标跟踪算法DeepSORT解决无人机对目标的定位问题。采用SiamRPN算法实现高精度定位,满足电力巡检业务需求,快速批量识别目标。
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