基于深度学习的自校准雷达测速系统的研究

Development of a Deep Learning-Based Self-Calibrating Radar Speed Measurement System

雷达测速仪作为主要的测速设备,在车速测量方面扮演着至关重要的角色。然而,雷达测速仪在实际应用中仍存在一定局限性,特别是长时间保持测速数值的准确性是一大挑战。研究一种基于卷积神经网络的自校准雷达测速系统,保证雷达测速仪的数据准确可靠。提出了一种低成本的神经网络测速系统,使用数字图像处理技术、YOLO v7目标检测神经网络,实现目标车辆的提取;另外,设计了一种速度计算卷积神经网络,制作数据集训练该神经网络,并通过数据消融实验确定了神经网络的参数。进行实验验证,结果表明该系统能够准确可靠地测量车速,并且具有较高的精度和稳定性。该速度计算卷积神经网络可以有效地识别出雷达是否存在误差,以及过滤掉雷达收集到的异常数值。并且,借助该功能可以实现雷达的自动校准功能,从而保持测速设备的长期准确,提高测速工作的可靠性和可持续性。

Radar speedometers,crucial in vehicular speed measurement,encounter challenges in maintaining accuracy over extended periods.This study introduces a self-calibrating radar speed measurement system employing a convolutional neural network(CNN)to enhance data accuracy and reliability.We present an economical neural network-based speed measurement system that utilizes digital image processing and YOLO v7 object detection neural network for target vehicle extraction.Additionally,a dedicated CNN for speed calculation is designed and trained using a custom dataset.The network's parameters are optimized through data ablation experiments.Experimental validation confirms the system's capability to measure vehicle speed with high accuracy and stability.The developed CNN for speed calculation effectively identifies potential radar errors and excludes anomalous data,thus facilitating the radar's automatic calibration.This feature ensures long-term precision of the speed measurement equipment,significantly improving the reliability and sustainability of speed measurement operations.