基于轻量级卷积神经网络的注油孔检测算法  

Oil Injection Hole Detection Algorithm Based on Lightweight Convolution Neural Network

作　　者：孟瑞[1,2] 卢宗远 丛英浩 MENG Rui;LU Zongyuan;CONG Yinghao(School of Mechanical Engineering,Anhui University of Technology,Maanshan 243032,China;Engineering Research Institute,Anhui University of Technology,Maanshan 243032,China;Shanghai Meishan Iron and Steel Limited by Share Ltd,Nanjing 210039,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243032 [2]安徽工业大学工程研究院,安徽马鞍山243032 [3]上海梅山钢铁股份有限公司,江苏南京210039

出　　处：《安徽工业大学学报（自然科学版）》2023年第2期166-172,190,共8页Journal of Anhui University of Technology（Natural Science）

基　　金：国家自然科学基金项目(51605005);安徽省高校自然科学重点基金项目(KJ2020A0244)。

摘　　要：为解决当前注油机器人目标检测算法对现场适应性弱、识别率低等问题,通过改进YOLOv5算法,提出一种新的注油孔检测算法YOLOv5-S。将ShuffleNetv2用于图像的特征提取,通过调整输入图像的分辨率以及扩大部分基本单元中深度卷积核尺寸,确保算法既具有轻量级网络结构又具有高精度的检测水平;采集不同工况下注油孔的图像,将其分类并标注,采用YOLOv5-S,YOLOv5,YOLOv3-tiny算法对其进行训练实验,验证提出算法的有效性。结果表明:YOLOv5-S在注油孔数据集上的检测精度保持在99.4%,与原算法相比,其模型容量压缩了77%,检测速度提升了11.7 F/s。本文提出的检测算法在工控机算力和存储资源有限的条件下具备良好的识别准确率和检测速度。In order to solve the problems of weak adaptability and low recognition rate of current target detection algorithm for oil injection robots,a new oil injection hole detection algorithm YOLOv5-S was proposed by improving YOLOv5 algorithm.ShuffleNetv2 was used as the feature extraction of the image,the resolution of the input image was adjusted,and the depth convolution kernel size in some basic units was expanded to ensure that the algorithm has both a lightweight network structure and a high-precision detection level.The collected oil injection hole images under different working conditions were classified and labeled,and YOLOv5-S,YOLOv5,YOLOv3 tiny algorithms were used to train them to verify the effectiveness of the proposed algorithm.The results show that the detection accuracy of YOLOv5-S on the oil injection hole dataset remains at 99.4%.Compared with the original algorithm,its model capacity is reduced by 77%,and the detection speed is increased by 11.7 F/s.The detection algorithm proposed in this paper has good recognition accuracy and detection speed under the condition of limited computing power and storage resources of industrial computer.

关 键 词：轻量级卷积神经网络 机器视觉 目标检测 注油孔 

分 类 号：O436[机械工程—光学工程]