改进YOLOV5模型的智能锯树机器人系统设计

殷洪海; 何律; 汪大海

改进YOLOV5模型的智能锯树机器人系统设计

引用本文：殷洪海，何律，汪大海.改进YOLOV5模型的智能锯树机器人系统设计[J].计算技术与自动化,2025,(2):177-182

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作者	单位
殷洪海，何律，汪大海	（常州晋陵电力实业有限公司,江苏常州 213000）

中文摘要:针对在锯树工程测量中遇到的环境复杂，耗时费力且效率不高等测量问题，设计了一种改进的深度学习算法（You Only Look Once, YOLO）模型的智能锯树机器人系统。通过无线传感网络，构建了无线传感器锯树工程测量拓扑结构，实现锯树工程测量供配电系统全方位数据监测；建立窄带物联网(Narrow Band Internet Of Things，NB IOT)分布式结构，提出采用改进YOLOV5算法对障碍物识别，使用超声波传感器和实验室虚拟仪器集成环境（Laboratory Virtual Instrument Engineering Workbench，LabVIEW）以及摄像机完成机器人的自主越障仿真操作，大大提高智能锯树机器人通信能力和计算能力。实验结果表明，改进后的YOLOV5算法模型在经过50000次迭代后，其准确率较改进前增加了9个百分点，召回率较改进前增加了8个百分点，在近距离障碍物识别上，准确率可达97.15%，且距离越近，其障碍物识别效果和自主越障效果越好。

中文关键词:智能锯树机器人 YOLOV5 超声波传感器 NB IOT CSMA 障碍物

Improved Design of Intelligent Sawing Robot System Based on YOLOV5 Model

Abstract:In view of the complex environment, time consuming and inefficient measurement problems encountered in sawing engineering measurement, this paper designs an intelligent sawing robot system based on an improved deep learning algorithm (You Only Look Once, YOLO) model. Through wireless sensor network, the wireless sensor sawing engineering measurement topology is constructed to realize the comprehensive data monitoring of power supply and distribution system of sawing engineering measurement. The narrow band internet of things (NB IOT) distributed structure is established, and the improved YOLOV5 algorithm is proposed to identify obstacles. Ultrasonic sensors, laboratory virtual instrument engineering workbench (LabVIEW) and cameras are used to complete the autonomous obstacle crossing simulation operation of the robot. Greatly improve the communication ability and computing power of intelligent sawing robot. The experimental results show that after 50,000 iterations of the improved YOLOV5 algorithm model, its accuracy rate has increased by 9 percentage points and the recall rate has increased by 8 percentage points compared with that before the improvement. In the short distance obstacle recognition, the accuracy rate can reach 97.15%, and the closer the distance, the better the obstacle recognition effect and autonomous obstacle clearing effect.

keywords:intelligent tree sawing robot YOLOV5 ultrasonic sensor NB IOT CSMA obstacles

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