[1]曹振锋,王明泉,路宇鹏,等.基于 YOLOv8 的轻量化轮毂缺陷检测算法研究[J].机械与电子,2025,(06):31-36.
　CAO Zhenfeng,WANG Mingquan,LU Yupeng,et al.Research on the Lightweight Wheel Hub Defect Detection Algorithm Based on YOLOv8[J].Machinery & Electronics,2025,(06):31-36.
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基于 YOLOv8 的轻量化轮毂缺陷检测算法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年06期
页码:
31-36
栏目:
自动控制与检测
出版日期:
2025-06-27

文章信息/Info

Title:
Research on the Lightweight Wheel Hub Defect Detection Algorithm Based on YOLOv8
文章编号:
1001-2257 ( 2025 ) 06-0031-06
作者:
曹振锋; 王明泉; 路宇鹏; 吴志成; 王晋华
中北大学信息与通信工程学院,山西 太原 030051
Author(s):
CAO Zhenfeng ;  WANG Mingquan ;  LU Yupeng ;  WU Zhicheng ;  WANG Jinhua
( School of Information and Communication Engineering , North University of China , Taiyuan 030051 , China )
关键词:
缺陷检测;  StarNet ;  YOLOv8 ; 卷积注意力融合模块
Keywords:
defect detection ;  starNet ;  YOLOv8 ;  CAFM
分类号:
TP391
文献标志码:
A
摘要:
针对现有的轮毂内部缺陷检测准确度有限,模型复杂度高的问题,提出一种基于 YOLOv8 的轻量化轮毂缺陷检测算法 SNCL-YOLO 。首先,采用 StarNet 网络架构构造 C2f-STA 模块,取代原骨干网络中的 C2f 模块,降低存储需求并且提升计算速度,以提高检测效率;其次,在颈部网络中引入卷积注意力融 合模块( convolution and attention fusion module , CAFM ),增强全局和局部的特征建模;最后,将原检测头替换,采用 LSCD-Head 轻量级共享卷积检测头,减少网络参数量和计算量。实验结果表明,改进后的模型参数量减少 11.9% ,计算量减少 9.8% ,平均精度均值( mAP )从 90.7% 提升到了 94.1% ,增加 3.4 百分点。该模型在计算量以及参数降低情况下,有效增强了铝合金轮毂缺陷检测模型的检测性能。
Abstract:
In order to solve the problems of limited accuracy and high complexity of model existed in the internal defect detection of wheel hubs , a lightweight wheel defect detection algorithm SNCL-YOLO based on YOLOv8 is proposed.Firstly , the StarNet network architecture is used to construct the C2f-STA module to replace the C2f module in the original backbone network , which reduces the storage requirement and improves the computing speed to improve the detection efficiency.Secondly , the convolution and attention fusion module ( CAFM ) is introduced into the neck network to enhance the global and local feature modeling.Finally , the original detection head is replaced by LSCD-Head lightweight shared convolutional detection head , which reduces the number of network parameters and the amount of computation.The experimental results show that the number of parameters of the improved model is reduced by 11.9% , the amount of calculation is reduced by 9.8% , and the mean average precision ( mAP ) is increased from 90.7% to 94.1% , with an increased mAP of 3.4 percentage points.The model effectively strengthens the detection performance of the aluminum alloy wheel defect detection model under the reduction of computational cost and parameter.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2025-01-07 基金项目:国家自然科学基金资助项目( 61171177 ) 作者简介:曹振锋 ( 2001- ),男,河南许昌人,硕士研究生,研究方向为图像处理;王明泉 ( 1970- ),男,山西朔州人,博士,教授,博士研究生导师,研究方向为图像处理、工业检测与识别等。
更新日期/Last Update: 2025-07-03