摘要
针对微小型无人机实时车辆检测中的体积、能效及复杂背景问题,提出一种基于FPGA的位置加权梯度直方图(PWGH)与支持向量机(SVM)的低成本方案,将传统HOG的9-bin扩展为9×2复合直方图,提升特征区分度,在UAV_ROD数据集上准确率提高了8.4%,在Zynq7020硬件平台实现50 f/s的处理速度,功耗仅为2.3 W,验证了该方案在精度、速度、低功耗及小型化方面的优势,具有一定的工程应用价值。
To address the challenges of size constraints,energy efficiency,and complex background environments in real-time vehicle detection for micro unmanned aerial vehicles(UAVs),this paper proposes a low-cost solution based on Field-Programmable Gate Arrays(FPGA)that integrates a Position-Weighted Gradient Histogram(PWGH)with Support Vector Machines(SVM).The method extends the traditional 9-bin Histogram of Oriented Gradients(HOG)into a 9×2 composite histogram to enhance feature discrimination.Evaluated on the UAV_ROD dataset,the approach achieves an 8.4%improvement in accuracy.Implemented on the Zynq7020-mini platform,it processes at 50 frames per second with a power consumption of only 2.3 W.The experimental results validate the scheme's advantages in accuracy,processing speed,low power consumption,and miniaturization,demonstrating significant potential for practical engineering applications in UAV-based real-time vehicle detection.
作者
杨志强
刘凤丽
郝永平
杨丽圆
丁瑞书
YANG Zhiqiang;LIU Fengli;HAO Yongping;YANG Liyuan;DING Ruishu(School of Mechanical Engineering,Shenyang Ligong University,Shenyang 110159,China;School of Equipment Engineering,Shenyang Ligong University,Shenyang 110159,China)
出处
《集成电路与嵌入式系统》
2025年第10期66-74,共9页
Integrated Circuits and Embedded Systems
基金
辽宁省教育厅科学研究资助项目(LJ2019007)。
