信息与控制2026，Vol.55Issue(4)：652-664,13.DOI:10.13976/j.cnki.xk.2025.0162
基于YOLOv9的露天矿电铲的轻量化实时斗齿状态检测方法
Lightweight Real-time Detection Method for Bucket Teeth Status of Electric Shovels in Open-pit Mines Based on YOLOv9
摘要
Abstract
As the core equipment in open-pit mining production,electric shovels are prone to problems such as gear sleeve detachment and breakage during operation.When the fallen parts mix into the ore,it can easily cause accidents such as crushing equipment jamming,damage,or belt tearing.To solve these problems,we propose a lightweight and high-precision model based on the improved YOLOv9-s model.By introducing the FasterNet backbone network,improving the AConv module in the neck network with dual convolution(DualConv),and re-lightweighting P-RepNCSPELAN4 with partial convolution(PConv),the proposed model's feature extraction ability is ensured while reducing the number of parameters and floating-point operations.The results show that FDP-YOLO model reduces the weight by 36.2%,the number of parameters by 36.2%,the floating-point operation GFLOPs by 48.1%,and mAP@50 reaches 99%.The status detection method can satisfy the intelligent application need of open-pit mine electric shovel tooth detection.关键词
斗齿/目标检测/轻量化/FasterNet/DualConv/PConvKey words
bucket tooth/object detection/lightweight/FasterNet/DualConv/PConv分类
矿业与冶金引用本文复制引用
刘晓松,荆洪迪,李歆,潘福成,于庆磊,张兴帆..基于YOLOv9的露天矿电铲的轻量化实时斗齿状态检测方法[J].信息与控制,2026,55(4):652-664,13.基金项目
国家自然科学基金项目(U21A20106) （U21A20106）
