测控技术2025，Vol.44Issue(6)：32-39,8.DOI:10.19708/j.ckjs.2025.04.219
基于特征增强与知识蒸馏的汽车漆面检测
Automotive Paint Surface Detection Based on Feature Enhancement and Knowledge Distillation
摘要
Abstract
In order to address the challenges of insufficient accuracy and sluggish speed in automotive paint surface defect detection,an automotive paint surface detection method based on YOLOv8 is proposed.Firstly,the convolutional block attention module(CBAM)within the Neck section of the YOLOv8 network is embeded,which effectively mines both channel and spatial feature information pertaining to automotive paint defects.Sec-ondly,the conventional upsampling module in the Neck section of the original YOLOv8 network is substituted with a lightweight content-aware reassembly of features(CARAFE)module.This substitution mitigates the loss of feature information during upsampling,generates more intricate details and smoother edges,and notably ex-pands the model's receptive field.These optimizations significantly bolster the model's ability to capture paint surface defect features,thereby enhancing detection accuracy.Lastly,knowledge distillation(teacher-student model)is applied to the improved YOLOv8 network,with the improved YOLOv8 algorithm as the teacher mod-el and a lightweight YOLOv8s serving as the student model.Leveraging a lead-learn-collaborate strategy,saving computational cost and enhancing detection speed are achieved.Experimental results demonstrate that the im-proved YOLOv8 achieves a mean average precision of 92.6％for automotive paint surface defect detection,marking a 3.7％improvement over the original YOLOv8.Furthermore,the refined model has a parameter count of 1.1 × 10 6,which is 2.1 × 10 6 fewer than YOLOv8.Additionally,the model transmits more frames per second than YOLOv8 and maintains a detection speed of 149 f/s,which meets the requirements of real-time recogni-tion.It can effectively solve the problems of low accuracy and slow detection speed in automotive paint defect detection,demonstrating the effectiveness of the automotive paint defect detection algorithm.关键词
漆面缺陷/YOLOv8/CBAM模块/知识蒸馏Key words
paint defects/YOLOv8s/CBAM module/knowledge distillation分类
计算机与自动化引用本文复制引用
王剑楠,胡璐萍,管声启,白永平,臧凯,林文彩..基于特征增强与知识蒸馏的汽车漆面检测[J].测控技术,2025,44(6):32-39,8.基金项目
陕西省教育厅科研计划项目(23JK0531) （23JK0531）
