基于YOLOv5算法的炮管内壁污渍识别与定位技术  

Stain Recognition and Location Technology of Gun Barrel Inner Wall Based on YOLOv5 Algorithm

作　　者：冷祥智 陶卫军[1] Leng Xiangzhi;Tao Weijun(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院,南京210094

出　　处：《兵工自动化》2024年第4期1-6,共6页Ordnance Industry Automation

摘　　要：为准确评估炮管内壁的清理情况,采用YOLOv5人工神经网络结合机器视觉对清理后的155mm口径炮管内壁的污渍进行实时检测。考虑到污渍主要分为油污与残留的铜渍2种,在检测任务中通过图像识别技术分别对污渍进行种类识别、定位以及对污渍面积判定;利用图像像素信息与外部环境信息,基于单目摄像头采集的视频图像,采用改进并训练后的YOLOv5人工神经网络模型作为识别工具对炮管内壁进行实时图像识别。实验结果表明:该检测系统能较好地完成目标检测任务,对目标定位误差控制在5 cm内,满足炮管内壁自动清理中的内壁污渍定位要求。In order to accurately evaluate the cleaning condition of the inner wall of the gun barrel,the YOLOv5 artificial neural network combined with machine vision was used to detect the stains on the inner wall of the cleaned 155 mm caliber gun barrel in real time.Considering that the stains are mainly divided into oil stains and residual copper stains,the image recognition technology is used to identify and locate the types of stains and determine the area of stains in the detection task;Using the image pixel information and the external environment information,based on the video image collected by the monocular camera,the improved and trained YOLOv5 artificial neural network model is used as the recognition tool to carry out the real-time image recognition of the inner wall of the gun barrel.The experimental results show that the detection system can complete the target detection task well,and the target positioning error is controlled within 5 cm,which meets the requirements of the inner wall dirt positioning in the automatic cleaning of the inner wall of the gun barrel.

关 键 词：YOLOv5 炮管内壁 单目摄像头 污渍检测 

分 类 号：TJ303[兵器科学与技术—火炮、自动武器与弹药工程]